3691 results about "Data harvesting" patented technology

The invention belongs to the technical field of knowledge maps and artificial intelligence, and discloses a building safety risk identification method of a big language model assisted knowledge map. According to the technical scheme, the overall process comprises the steps of data collection and preprocessing, construction of urban building structured table data, construction of an urban building safety knowledge graph, integration of the structured table data and knowledge graph data, model fine adjustment, model training, model performance evaluation and model recognition effect verification. According to the technical scheme, the large language model with the high semantic modeling capacity and the knowledge graph with the high graph structure expression capacity are integrated, the related knowledge of urban building safety is automatically extracted, constructed and integrated, high-risk events such as fire disasters and structural hidden dangers are recognized, and the informatization level and the intelligent level of urban building safety management are improved.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention provides a multi-agent control system based on a large language model. The system comprises an analogue simulation subsystem and a coordination control subsystem. The analogue simulation subsystem comprises a data acquisition module, an initial reward function generation module, an error correction module, a dense reward function generation module and a strategy network updating module, wherein the data acquisition module is used for acquiring a multi-agent reinforcement learning training code; the initial reward function generation module is used for generating an initial reward function code; the error correction module is used for generating executable reward function codes; the dense reward function generation module is used for generating dense reward function codes; and the strategy network updating module is used for obtaining the strategy network with the maximized reward. The coordination control subsystem comprises a data collection module and a strategy distribution module; wherein the data collection module is used for receiving observation data; and the strategy distribution module is used for guiding multiple agents to execute a control task by adopting a strategy network generation action for maximizing rewards based on the observation data.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention relates to the technical field of information security, and discloses a network information security monitoring system, which comprises the following modules: a data collection module used for collecting data from network traffic, system logs and user behaviors in real time; the data processing module adopts a Z-score standardized processing technology to unify different features to the same scale so as to ensure that the mean value of the features is 0 and the standard deviation is 1, thereby improving the comparability between the features; and the threat detection module analyzes the processed data based on a machine learning algorithm and identifies possible network security threats. Through cooperative work of all the modules, comprehensive real-time data collection, accurate threat detection, practical response execution and continuous feedback optimization are realized, the network security threat handling capacity is integrally improved, and network information security monitoring work is stably and efficiently carried out for a long time. The problem that a response strategy of a traditional network information security monitoring system lacks pertinence and timeliness is solved.

The invention discloses an LSTM-COF-based power equipment data anomaly detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the following steps: collecting historical data, and constructing a three-dimensional data matrix; predicting equipment parameters at the extreme temperature through an LSTM model; compressing the features, quantifying the covariance deviation degree between the features in combination with a correlation abnormal factor algorithm, and detecting abnormal points; the system integrates a data collection module, a data preprocessing module, a data prediction module, a detection model generation module and a visualization module. According to the method, the multi-dimensional historical operation data of the power equipment is collected, the equipment parameters in the extreme temperature environment are predicted by using the LSTM model, the principal component analysis dimensionality reduction and correlation abnormal factor algorithms are combined, insulation degradation type and electrical connection type faults can be dynamically identified, the data distribution change is adapted through the incremental learning mechanism, and the fault diagnosis accuracy is improved. The problems of low high-dimensional data processing efficiency and poor anomaly detection adaptability due to manual experience dependence in a traditional method are solved.

The invention discloses a database question and answer model training method and device, a storage medium and computer equipment, and the method comprises the steps: associating standard structured query language statements, standard execution result answers and standard natural language questions, and generating training annotation data; and collecting simulation derivation problems possibly proposed for the database to obtain non-labeled data for training. Based on a GRPO reinforcement learning framework and a scoring reward function provided by a double-tower model, training is carried out on the scoring reward function by utilizing training labeling data, supervised fine tuning training is carried out on a database question and answer model, and non-labeling data for training, format rewards, executable rewards and scoring rewards of the scoring reward function are combined, so that the scoring reward function of the database question and answer model is obtained. And continuing to train the database question and answer model after supervised fine tuning training. Preliminary training is carried out through a small amount of annotation data, then subsequent training is carried out through non-annotation data, the reasoning ability of the model can be stimulated, the annotation cost is reduced, and the training efficiency is improved.

The invention discloses a transformer fault detection device based on a fuzzy logic algorithm, and the device comprises a data collection module which obtains the operation original data of a transformer in real time through combining the dissolved gas in oil with the temperature, vibration, current and voltage; the data preprocessing module is used for carrying out missing value processing, noise removal and abnormal value detection and processing on the original data; the feature extraction module is used for realizing dynamic feature selection based on data analysis provided by the data preprocessing module; the fault identification module is used for carrying out abnormal waveform judgment on current, voltage, temperature and vibration parameters through a threshold calculation unit and carrying out threshold adjustment based on an optimization algorithm; and the fault detection module triggers the alarm unit or maintains a normal working state according to an identification result of the fault identification module. According to the invention, through monitoring analysis and timely alarm notification, accurate and efficient monitoring of the transformer fault is realized, and stable operation and long-term reliability of the transformer are ensured.

A federated learning system (100) for data protection in data sharing and collaboration, consisting of: a module for data acquisition and local preprocessing that is configured to clean, normalize and standardize local data sets at each participating node without transferring raw data externally; a local model training module configured to train a machine learning model on the pre-processed local dataset; a secure model update and encryption module configured to encrypt and secure model parameters or updates before transmission using privacy protection techniques; a federated aggregation and coordination module configured to aggregate encrypted updates from multiple participating nodes into a global model; a module for monitoring and ensuring data protection compliance, configured to enforce data protection budgets and audit protocols and to ensure compliance with data protection regulations; a performance optimization and resource management module configured to optimize communication, computation, and resource utilization across all nodes; and a module for global model delivery and feedback, configured to redistribute the aggregated global model to participants and integrate performance feedback for iterative improvements.

The invention relates to the field of methane change dynamic traceability analysis, and discloses a methane change dynamic traceability analysis and early warning system based on artificial intelligence, and the system comprises a data collection module which collects methane concentration data, constructs a two-state three-dimensional monitoring network, and calculates a relative standard deviation; the multi-source analysis module is used for merging calibration environment data, generating a three-dimensional matrix, iteratively updating a class cluster mean value and judging whether a burst leakage traceability process is triggered or not; capturing a time sequence dependency relationship, adding abnormal data optimization, analyzing and triggering residual drive and retraining; the anomaly recognition module is used for constructing a four-dimensional source feature matrix and dynamically adjusting the source feature weight; constructing an observation data matrix, and solving the contribution proportion of each source to the methane concentration; iteratively optimizing the source intensity parameter until convergence; and the early warning response module is used for dynamically updating a historical residual mean value and a standard deviation, judging a response level, reversely tracking an air mass track, positioning a potential emission source coordinate, and triggering a corresponding early warning response if an abnormal degree condition is met.

The invention discloses an LCA-based fabricated building carbon neutralization analysis management system, and particularly relates to the field of analysis management, and the system comprises a data collection module, a life cycle analysis module, a carbon emission calculation module, a carbon neutralization strategy generation module, a visual display module, and a user feedback and optimization module. According to the invention, through integration of an Internet of Things sensor, a BIM model and a block chain technology, full life cycle links are automatically collected; the system quantifies the environmental influence of each stage by using a dynamic LCA model, calculates the carbon emission in real time, and generates a carbon neutralization strategy based on a multi-objective optimization algorithm, including renewable energy application and carbon compensation measures; through a visual tool and dynamic simulation, the system visually displays an analysis result and a strategy effect, and supports user interaction and parameter adjustment; a user feedback mechanism is combined with a reinforcement learning algorithm, system parameters and strategies are dynamically optimized, a'feedback-optimization-re-feedback 'closed loop is formed, and analysis precision and strategy feasibility are improved.

The invention discloses a security camera lens production process dynamic multi-parameter process monitoring method, and relates to the technical field of multi-parameter process monitoring. The method comprises the following steps: collecting process parameters and waviness characteristic data; collecting optical characteristics and physical response data of security camera lens raw materials; discretizing the data, and generating raw material standard data according to material types and performance; collecting real-time polishing data, and calculating to obtain a polishing deviation factor vector; calculating a weight through an entropy weight method to obtain a polishing alarm correction index, giving an alarm and adjusting polishing parameters when the polishing alarm correction index exceeds a threshold value, and obtaining corrected geometric feature data; the method comprises the following steps: collecting data in a coating stage, obtaining coating material response and processing environment data through a two-dimensional feature extraction method, and respectively calculating a material response deviation index and a processing environment disturbance index; and combining the two indexes to obtain the comprehensive deviation index of the coating process. According to the comprehensive deviation index of the coating process, the production process of the security camera lens is monitored in real time.

The invention discloses an equipment state monitoring method based on multi-source information fusion, and the method comprises the steps: enabling a real-time sensor to collect the operation parameter data, vibration parameter data and environment parameter data of equipment, collecting the historical state data of the equipment with a recording label, and carrying out the preprocessing of the data; time domain features, frequency domain features and working condition features of the data are extracted according to a layering mode, and dynamic weight coefficients of all the features are set; calculating a reference threshold value by adopting a specific model; evaluating the health condition of the current equipment by adopting a depth measurement method, calculating an equipment health factor, and calculating a trend compensation item; and obtaining an equipment state monitoring dynamic threshold based on the reference threshold, the health correction item and the trend compensation item. The invention further discloses an equipment state monitoring device based on multi-source information fusion, corresponding equipment and a storage medium. According to the equipment state monitoring method based on multi-source information fusion provided by the embodiment of the invention, the accuracy, real-time performance and reliability of equipment state monitoring can be effectively improved.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and / or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically control movement of a vehicle's component parts on a job site to perform tasks. The techniques may include using an MPC-based Control System to perform a cycle of training and deployment of a predictive model specific to a particular earth-moving vehicle to control that vehicle's autonomous operations, and to further using resulting data in additional manners—such a cycle may include using a data gathering module on the vehicle to gather actual operational data of the vehicle during manual control of the vehicle on job site(s) by human operator(s) during performance of task(s), generating a 3D site map modeling the vehicle's surroundings, training a Model Predictive Control (MPC) model based on the actual operational data and 3D site map, and deploying the trained model to the vehicle for use in autonomous operations.

The invention discloses a self-adaptive budget report generation method based on multiple agents, and belongs to the technical field of report generation, and the method comprises the steps: according to a collected budget table sample, analyzing the agents through the table sample, obtaining a table sample structure based on image recognition, recognizing a basic index field, and carrying out semantic matching according to the basic index field to obtain analysis logic; according to the analysis logic, basic data are collected through a data collection agent, and analysis tasks are allocated to all professional agents through a task allocation agent, so that the basic data are analyzed; and generating a budget report through the large model according to the basic data and the analysis result. Through the image recognition technology and semantic matching of the table sample analysis agent, template-free dynamic table sample analysis is realized, the structure and the field are automatically extracted, and the manual configuration cost is reduced. The data collection agent integrates multi-source heterogeneous data, and the task distribution agent dynamically schedules professional agents for division and cooperation, so that the analysis accuracy and efficiency are improved.

The invention relates to the technical field of mineral resource estimation, and discloses a mineral reserve estimation method based on artificial intelligence, comprising the following steps: step 1, collecting and preprocessing data, acquiring multi-source data, and performing data standardization and feature extraction; 2, carrying out ore body modeling through a 3D convolutional neural network, constructing the 3D convolutional neural network, and carrying out ore body prediction through three-dimensional convolution calculation; step 3, optimizing ore body space prediction in combination with Kriging interpolation, preliminarily predicting ore body space distribution through Kriging interpolation, and further optimizing by using a deep learning model; and step 4, estimating reserves based on Bayesian optimization. The technical scheme of combining multi-source data fusion, a 3D convolutional neural network and Kriging interpolation is adopted, the effect of accurately capturing ore body space distribution under the complex geological condition is achieved, and compared with an existing scheme depending on a traditional geological statistical method, the problem that the fault zone and heterogeneous region modeling capacity is insufficient is solved.

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

The invention belongs to the technical field of wall painting restoration, and discloses a damaged image automatic digital restoration method comprising the following steps: a data collection module collects geological data and image data; the three-dimensional structure scanning unit obtains a three-dimensional structure of the surface of the mural and space coordinates of a damaged area; the infrared imaging unit extracts a bottom layer pattern; the intelligent damage detection module locates a damage area; the multi-modal generative repair module complements a damaged physical form through a structural layer repair unit, and the repair scheme planning module plans an entity repair route and a material scheme; the damaged area color partitioning and boundary extraction module is used for performing partitioning and boundary extraction on the color for repairing the damaged area; and the entity repair execution module completes physical repair. According to the invention, through linkage of entity repair and digital achievements, the entity repair execution module performs physical repair based on a digital repair model, so that excessive dependence of traditional repair on artificial experience is broken through, and historical authenticity and artistic integrity of the mural are guaranteed by a scientific method.

The invention discloses a personal obesity risk prediction system and method based on AI of big data, and belongs to the field of medical health, and the system comprises a data collection module, a multi-dimensional feature construction module, a risk label dynamic generation module, a model training and risk prediction module, a credibility evaluation and calibration module and the like. The system collects multi-source heterogeneous data through wearable equipment, a biochemical interface and a health platform API (Application Program Interface), uniformly encodes the multi-source heterogeneous data into a standard time sequence and then constructs behavior-metabolism-environment coupling characteristics. And performing joint modeling on the dynamic features and the labels by adopting a graph neural network in combination with causal factorization, and outputting an individual obesity risk prediction result. The result credibility is improved through Monte Carlo Dropout and a confidence interval calibration mechanism, and calibration information is fed back to a feature construction link to optimize a modeling strategy. Finally, the key risk factors are presented in the form of a visual thermodynamic diagram and a causal path diagram, and an individualized intervention suggestion vector is generated. The method has the beneficial effects of improving prediction accuracy and enhancing individual intervention pertinence.

A data generation apparatus includes: a transmitter configured to transmit request information for collecting data to a model deployment apparatus; and a receiver configured to receive AI / ML model-related information from the model deployment apparatus; wherein the transmitter is further configured to transmit data to the model deployment apparatus according to the AI / ML model-related information.

The invention discloses a cast-in-situ bored pile digital construction control method which comprises the following steps: S1, collecting historical data, and establishing a cast-in-situ bored pile construction information database; s2, geological modeling is established, and a pile hole area three-dimensional stratum and soil layer distribution corresponding to all pile holes are generated in a fitting mode; s3, according to the cast-in-situ bored pile construction information database, finding out average construction efficiency and cost data under various hole forming processes of similar stratums in various stratums of a pile hole area, and calculating the construction period and cost of different hole forming processes of a single pile for construction management personnel to select the hole forming processes and equipment specifications; s4, monitoring early warning and auxiliary decision making in construction; and S5, post evaluation analysis. According to the system and the method, key construction parameters of the bored pile are automatically monitored by relying on core construction equipment and devices, the production dynamic state of the bored pile is visually displayed through online monitoring and early warning of the key parameters by the system and the platform, a friendly interactive interface is provided for operators, and construction parameter selection is assisted.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention belongs to the technical field of hydraulic engineering measurement, and discloses a seawall measurement method and system based on multi-source data fusion, and the method comprises the steps: collecting geological data and hydrological data through a data collection module; the route planning module formulates a route; the overwater data acquisition equipment and the underwater data acquisition equipment are used for monitoring; the auxiliary equipment provides a high-precision positioning reference; the meteorological data module obtains meteorological data; the data processing module processes the data; the three-dimensional modeling module carries out point cloud registration and DEM model construction; the comprehensive analysis module performs comprehensive evaluation and predicts potential problems and risks; when an abnormal condition or a potential risk is monitored, the early warning module gives an alarm. Multi-source data acquisition equipment such as unmanned aerial vehicle aerial survey, a laser radar, a multi-beam depth finder, a side-scan sonar, a shallow profiler and a micro-motion exploration instrument are integrated, seawall overwater and underwater full-dimensional data acquisition, processing, modeling and volume calculation are realized, and high precision and reliability of measurement results are ensured.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

The invention discloses a central air conditioner energy consumption big data intelligent analysis method based on data mining. The method comprises the following steps that historical data of central air conditioner energy consumption is collected; performing data preprocessing on historical data of the energy consumption data of the central air conditioner; establishing an analysis engine, namely establishing an energy consumption model and establishing a load prediction model; establishing a central air conditioner energy efficiency detection strategy model; establishing a fault prediction model; after energy consumption data of the central air conditioner are collected for data preprocessing, energy efficiency detection is conducted through the energy efficiency detection strategy model of the central air conditioner according to predicted energy consumption and load data output by an analysis engine, fault prediction is conducted through the fault prediction model, and finally the data are summarized to obtain an analysis report. Through data mining and intelligent analysis, the problem that in the prior art, data dimensions are complex is solved, and the problem of overfitting of the position where the central air conditioner is located due to building specificity is solved.

The invention relates to the technical field of disaster relief path planning, in particular to a disaster relief path planning and mode optimization method and system based on a large model, and the method comprises the following steps: collecting and preprocessing data; constructing a prediction model; planning and optimizing a path; a decision support system; real-time adjustment and feedback are carried out; the method has the beneficial effects that the disaster condition and the influence thereof can be quickly analyzed by using big data and a machine learning technology, so that the time required by traditional rescue path planning is greatly shortened, and the emergency response speed is improved. Within golden 72 hours of post-disaster rescue, each second is crucial. The system can provide an optimal rescue path and a resource allocation scheme in an extremely short time, help rescue workers to quickly arrive at a disaster area, and save more lives.