335 results about "Expert system" patented technology

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

An intelligent fault-in-rail analysis method based on multimodal fusion learning. Ultrasonic analysis data of the rail and high-definition rail surface images taken by line scan cameras are exploited in combination to detect and display internal and surface faults of the rail through different renditions using multimodal data. Rail DEtection TRansformer (R-DETR) technology or a You Only Look Once (YOLO) inspection machine-learning algorithm are employed in a deep convolutional neural network to analyze B-scan and rail surface image data to recognize rail line faults. After obtaining expressive data through data enhancement by computer processing, faults are recognized and pinpointed using the deep convolutional neural network learning algorithm. Analysis results are corrected with expert systems. The disclosed automatic ultrasonic fault-in-rail inspection solves the problem of slow detection and difficulties in tracking faults in everyday routine maintenance while also protecting the safety of rail operation and maintenance personnel.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention discloses a high-voltage power distribution station room dynamic environment and equipment state monitoring system and method. The system comprises a multi-dimensional data perception acquisition integration module, a heterogeneous data feature extraction and fusion transmission module, a pre-analysis module based on composite weight distribution, an association mapping module combined with historical data, a core diagnosis module based on an expert system rule base and a multi-modal visual output module. Multi-source data such as temperature and humidity, gas concentration, vibration, voltage and current are collected, after feature extraction and fusion, a data potential relation is mined by using composite weight distribution and historical data association, then equipment state evaluation and fault diagnosis are performed based on an expert system rule base, and finally, visual display and early warning output are performed in a multi-mode form. The system and the method can comprehensively monitor the environment and the equipment state of the power distribution station house, accurately diagnose faults, and effectively improve the operation safety and the operation and maintenance efficiency of the high-voltage power distribution station house.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention relates to the field of broadband alarm, in particular to a broadband user-based optical modem power failure and fiber break alarm real-time monitoring method, which comprises the following steps of: S1, monitoring and recording the online state, the light attenuation level, the flow information, the packet loss rate, the network delay and the log of an optical modem under OLT (Optical Line Terminal) equipment in real time; s2, automatically identifying an abnormal mode and a potential fault according to a model established by a machine learning algorithm and a deep learning algorithm; s3, automatically diagnosing problem reasons according to the collected data, and optimizing the knowledge base in combination with an expert system; s4, performing deep mining and analysis on historical fault data, and predicting a potential fault risk based on the historical data and a real-time monitoring result; and S5, continuously updating and optimizing a fault detection and diagnosis model through a self-learning algorithm model, and uniformly monitoring and managing all data acquisition, analysis and alarm modules. The fault detection accuracy and timeliness can be improved, and potential fault prediction and preventive maintenance are realized.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention relates to an optimization method of a hybrid expert system, computer equipment, a readable storage medium and a program product. A plurality of experts contained in the MOE are deployed in a plurality of artificial intelligence chips in groups, and the method comprises the following steps: carrying out routing calculation on an original input tensor to obtain a routing calculation result; determining input element grouping information corresponding to each expert based on expert index information and expert weight information in the routing calculation result; taking the expert dimension as a parallel dimension, executing rearrangement operation for the original input tensor in parallel based on the input element grouping information, taking a rearrangement result as input information of an expert, executing matrix multiplication and accumulation operation, and taking the expert dimension as the parallel dimension, and based on the input element grouping information, executing anti-rearrangement operation of the matrix multiplication and accumulation operation result in parallel to obtain a final operation result. By adopting the method, the MOE reasoning performance can be improved.

The invention discloses an unsupervised industrial defect detection method, system and device based on a multi-mode expert system. The unsupervised industrial defect detection method comprises the following steps: acquiring a label-free industrial defect image data set and a text cue word; inputting into a multi-modal expert system to obtain a corresponding prediction result; screening out a preliminary labeling result; filtering the preliminary labeling result based on a prior constraint to obtain a fine label; inputting the industrial defect image data set and the fine label into a self-supervised model for training, and outputting a pseudo label after training is completed; performing double filtering on the pseudo labels by adopting a dynamic threshold value and a prior constraint; inputting the industrial defect image data set and the pseudo label after the current training double filtering into the self-supervised model after the last training for re-training, outputting the pseudo label, and returning to execute the double filtering until the trained self-supervised model is obtained; and predicting an industrial defect image to be detected by adopting the trained self-supervised model to obtain a corresponding industrial defect detection result. And the detection accuracy and efficiency are improved.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention discloses an automatic report generation method based on cross-modal agent collaboration, and relates to the technical field of automatic document generation, and the method comprises the steps that a central scheduling engine coordinates an outline generation agent, a data query agent, a chart drawing agent and a data analysis agent; the central scheduling engine drives the outline generation agent to generate a structured framework of a report based on a user demand, and dynamically schedules each agent according to a task dependency relationship; the data query agent realizes data retrieval by fusing retrieval enhancement generation and a thinking chain technology; the chart drawing agent adaptively generates a chart according to the data type; the data analysis agent is combined with an expert system and a knowledge base to generate an analysis conclusion; wherein the outline generation agent, the data query agent, the chart drawing agent and the data analysis agent are coordinated in parallel under the coordination of the central scheduling engine, and exception handling and dynamic adjustment are realized through a cross-agent coordination mechanism.

The invention discloses a method for intelligently designing a tunnel grouting scheme and an expert system. The method comprises the following steps: step 1, establishing a three-dimensional geological tunnel model; 2, grouting design parameters and the value range of the grouting design parameters are determined, a grouting design parameter set is generated randomly, and numerical simulation is conducted; 3, generating a simulation result parameter set, and combining the simulation result parameter set with the grouting design parameter set to form a data sample; various simulation result parameters in the data sample are set as input layer data, and various grouting design parameters are set as output layer data; 4, repeating the step 2 and the step 3, generating a data sample set and training a neural network prediction model; 5, establishing a plurality of independent neural network models according to different geology and excavation face characteristics, and integrating the models to an expert system; and step 6, inputting field parameters through an expert system, and intelligently generating a grouting scheme. The problems that an existing traditional method is low in efficiency, depends on manpower and lacks accuracy are solved.

The invention discloses a network traffic anomaly detection method and system based on a multi-modal coupling Mamba model and a mixed expert. The method comprises the following steps: collecting network traffic; cleaning the network flow data, and standardizing the data format; shunting the cleaned network traffic data based on quintuple (a source IP, a destination IP, a source port, a destination port and a protocol type) information; extracting data packet length, transmission direction and load byte information, constructing a data packet length sequence and a load byte sequence based on a time sequence view angle and an interaction view angle, and constructing a layered data packet interaction graph; the multi-mode flow representation is input into a coupling Mamba model, deep coupling and dynamic interaction are carried out among different modes, and high-level feature representation with higher identification capacity is generated; dynamically selecting and activating a plurality of expert sub-networks through a gating network by utilizing a hybrid expert system; and a final flow detection result is generated in combination with a result output by the MoE classifier and the original features, and confidence feedback is provided.

The invention belongs to the field of safety technology monitoring, and particularly relates to an electrical cabinet safety monitoring method and system. The temperature, the voltage and the current of the electrical cabinet are monitored in real time through the sensors, and data are transmitted to the information management system in the computer through the base station to be processed. An information processing system is arranged in the information management system, the information processing system has a preliminary fault judgment capability through an expert system, a model of the information processing system is trained through data transmitted by a sensor, and data belonging to faults complements the information processing system to serve as historical monitoring data. The real-time monitoring data is compared with historical monitoring data in a database so as to judge whether the electrical cabinet breaks down or not, and if the electrical cabinet breaks down, a buzzer of the calculator gives an alarm. In the practical application, the electrical cabinet is monitored in real time through the sensor, a worker is reminded of the failure of the electrical cabinet through the buzzer, the worker does not need to go to the electrical cabinet site to inspect, and manpower is greatly saved.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

The invention discloses a fruit and vegetable disease and insect pest spectrum database dynamic evolution updating and diagnosis optimization method based on model confidence feedback driving. Comprising the following steps: acquiring spectral data; based on the disease diagnosis model, outputting a corresponding model confidence coefficient; executing a calibration module, and outputting a calibrated model confidence coefficient; based on a preset confidence model, judging whether the model confidence is not less than a threshold value of high model confidence; based on the expert system module, judging whether the corresponding model confidence is not less than a threshold value of low model confidence; if so, executing an expert reexamination process; if not, inputting an abnormal sample library; and obtaining the number of newly added expert samples, a low confidence ratio and an interval to the last training time, executing an incremental training module, and outputting an instruction for updating and optimizing the disease and pest spectrum database and the abnormal sample library. And a data updating and model optimization mechanism for cooperative work of experts and models is established, so that misjudgment accumulation caused by excessive confidence of the models is effectively prevented, and the reliability of entering a database is ensured.

The invention relates to a time sequence knowledge graph reasoning method fusing mixed expert and cross-branch contrast learning. The method comprises the following steps: inputting a historical time sequence knowledge graph into a trained time sequence knowledge graph reasoning model for time sequence knowledge graph reasoning; wherein the time sequence mapping knowledge domain inference model comprises a double-branch time sequence modeling architecture, a dynamic hybrid expert system, a cross-branch contrast learning module and a decoding scoring module; respectively capturing dynamic change in a short period and a long-term evolution rule of an entity in the whole time span through a double-branch time sequence modeling architecture; structural features are extracted through a dynamic hybrid expert system, and cross-timestamp relation dependency is mined; mutual information between local and global time sequence views is maximized through a cross-branch contrast learning module, and fusion and learning of different levels of time sequence features are promoted; and the decoding scoring module is used for time sequence knowledge graph reasoning. According to the method, the modeling precision of the sudden change event and the periodic change is remarkably improved.

The invention provides a lithium battery K value real-time prediction method based on neural symbol reasoning and multi-modal learning, and the method specifically comprises the steps: S1, carrying out the coding of an industrial signal through a binary coding mode, converting an input voltage sequence into a binary sequence, and extracting a frequency domain feature through a wavelet transformation technology; s2, using a neural symbol inference engine to realize verifiable feature selection, and executing feature selection under logic constraints; s3, by means of a multi-head potential attention mechanism fusion process knowledge graph, a potential space projection matrix is constructed, and the number of attention heads is adjusted through a dynamic head number adjusting mechanism; and S4, completing online knowledge migration by utilizing a dynamic distillation expert system, and realizing knowledge transfer and model optimization by combining a hybrid expert architecture and an online distillation technology and applying an expert dynamic activation function and knowledge distillation loss. According to the method, advanced technologies such as neural symbol reasoning and multi-modal learning are fused, the prediction precision is high, the response delay is small, the energy consumption ratio is low, and the interpretability score is high.

The invention discloses an intelligent belt crack and tear detection system based on machine vision and laser coupling, and relates to the technical field of belt conveying. The intelligent belt crack and tear detection system based on machine vision and laser coupling comprises a visual detection module, an image processing flow module, a laser-assisted detection module and a data processing and control module, and the visual detection module comprises double-view-angle linear array cameras (a camera 1 and a camera 2), an area array monitoring camera and an intelligent light source system; the image processing flow module comprises multi-modal feature fusion, dynamic threshold segmentation, morphological filtering and deep learning classification; the laser-assisted detection module comprises a laser array sensor; the data processing and control module comprises an edge computing unit, an upper computer system, an integrated expert system, early warning and linkage control, system automation and intelligentization, operation and maintenance cost reduction, unmanned detection, full-process automatic operation, manual inspection frequency reduction and manpower cost saving by more than 50%.

Techniques for implementing a fuzzy logic expert system / service to control sending of a requested media file and / or control triggering of a remedial action (e.g., not sending the media file) are described. According to some examples, a computer-implemented method includes receiving a request at a content delivery service from a user for a media file, generating a piracy risk score for the media file by a fuzzy logic expert service of the content delivery service, sending the media file to the user based on the piracy risk score not exceeding a threshold, and blocking the sending of the media file to the user based on the piracy risk score exceeding the threshold.

The invention discloses a lightweight disease and insect pest early warning and capturing system, and the system comprises an execution unit which comprises an intelligent insect sticking plate, a trapping device, early warning equipment, and a controller; the acquisition unit comprises a multi-sensor array and an image acquisition device; the calculation unit comprises an edge processor, a data analyzer, an early warning calculation module, a configuration module and a decision module; the edge processor is configured with an image preprocessing method and a lightweight target detection and recognition algorithm; the early warning calculation module is configured with an insect pest counting algorithm, a density heat map generation method and a prediction model; the configuration module is used for configuring a threshold dynamic adjustment mechanism, an early warning grade division mechanism, an early warning trigger condition and a board change reminding mechanism; and the decision module is connected with the expert system. The technical problems of low manual inspection efficiency and limited coverage range are solved, the monitoring efficiency, the early warning precision and the prevention and control effect are improved, and the cost and risk of agricultural producers are reduced.

The invention relates to the field of big data analysis and artificial intelligence, in particular to a social public opinion perception and commodity demand prediction system and method based on multi-modal data, and the method comprises the steps: a multi-modal-text and image emotion fusion module extracts and processes text and image data through two-channel features, generates a text feature vector and an image feature vector, and carries out the feature extraction; a preprocessing module is used for fusing the multi-modal-text-graph emotion vector with the live broadcast activity data, the commodity historical sales volume and the live broadcast influence data to form a time sequence sensitive multi-modal-text-graph emotion fusion representation; the demand perception module performs adaptive feature conversion on fusion representation through a multi-layer perceptron to generate a multi-modal-text-graph fusion vector, and an expert system predicts commodity demands based on a decision theory in combination with sales data, label types and fan number information, so that effective extraction and bidirectional conversion of text and image modal features are realized.

The invention discloses a gear machine tool expert system construction method based on a generative rule, and the method comprises the steps: firstly carrying out the multi-dimensional modeling of a gear and a machine tool through an object-oriented technology, enabling a gear model to distinguish the characteristics of an axis position, a tooth curve and the like through an interface, and enabling a machine tool model to distinguish the machining technology and the precision grade through an interface; secondly, establishing a generation type rule mapping system from geometric, precision, material and functional attributes of the gear to machine tool movement, precision, process and rigidity attributes; finally, an intelligent reasoning system including forward reasoning, reverse verification, conflict coordination and self-learning iteration is constructed, and automatic and accurate matching from gear design requirements to machine tool optimization configuration is achieved. According to the method, the problems of high subjectivity and low efficiency caused by dependence on expert experience in the prior art are solved, and knowledge-driven intelligent decision making is realized.

The invention relates to the technical field of crossing of bioinformatics, artificial intelligence and plant genomics, and discloses a plant single cell gene expression prediction method, system, device and medium. A plant single cell gene expression prediction model realizes dynamic feature fusion of a DNA sequence and chromatin accessibility signals through a gated cross attention mechanism; the problem that a traditional single-mode model cannot model regulation and control dynamic association is effectively solved, and the result interpretability is enhanced; a hybrid expert system and a load balancing design are adopted to significantly improve the recognition capability of the model for rare cell types, and a decoupling prediction head design supports efficient transfer learning; a DNA long sequence processing mechanism and nucleosome scale feature coding ensure cross-species compatibility; an end-to-end automatic process and a dynamic parameter optimization framework greatly improve the practicability; a'prediction-verification 'closed-loop support system can be constructed for molecular breeding, and high-precision and interpretable prediction of plant single-cell gene expression is realized.

The invention discloses an intelligent examination and approval management method for the housing and construction industry, and the method comprises the steps: collecting examination and approval application data, carrying out the data cleaning, data deduplication and data standardization operation of the examination and approval application data, and obtaining preprocessed data; constructing a data classification model based on a machine learning algorithm, and performing material classification on the preprocessed data by using the data classification model to obtain a material classification result; according to a material classification result, performing primary processing and risk assessment on the preprocessed data by using an intelligent auditing system to obtain a risk score; performing secondary processing on the pre-processed data by using an expert system to obtain a personalized approval scheme; and examining and approving the preprocessed data according to the personalized examination and approval scheme, and storing the risk score and the examination and approval result to an intelligent examination and approval system. The invention relates to the technical field of examination and approval management, and solves the technical problems of low data quality, lack of flexibility and intelligence and the like of an existing residence construction examination and approval system.

The invention relates to the technical field of desulfurization, in particular to a desulfurization slurry control method and device based on a self-attention mechanism, and the method comprises the steps: collecting parameters of a desulfurization system in real time, building a variable correlation thermodynamic map, and constructing a hybrid prediction model for simulating chemical reaction kinetic characteristics by using a delayed feedback mechanism; outputting a multi-target variable prediction result in the desulfurization efficiency, the SO2 concentration at an outlet and the pH value of the slurry; generating a slurry supply strategy based on the multi-target variable prediction result and a pre-constructed slurry supply strategy optimization model in combination with a preset expert system rule; and dynamically optimizing a slurry supply strategy according to the operation state of the desulfurization system. Therefore, the problems that in the prior art, due to the fact that univariate adjustment and empirical parameter setting are generally taken as the principal thing, the overall modeling capacity for the multivariate coupling relation in the desulfurization process is lacked, control response lag is likely to be caused, the adjustment precision is insufficient, robustness to external disturbance is weak, and emission stability and operation economical efficiency are difficult to consider at the same time are solved.

The invention relates to the technical field of stereo garage safety management, and discloses a garage parking space safety risk monitoring method comprising the following steps: S1, multi-dimensional information real-time acquisition: comprehensively acquiring vehicle state, equipment operation data, environmental parameters and other multi-dimensional information through sensors at three levels of vehicle, equipment and environment; basic data is provided for subsequent analysis; and S2, data fusion and dynamic calibration: carrying out fusion processing on the collected multi-dimensional data, and introducing a dynamic calibration mechanism to correct the deviation of the sensor so as to improve the accuracy and reliability of the data. According to the garage parking space safety risk monitoring method, static and dynamic risk data are processed by integrating data models such as a support vector machine and a random forest, physical risks are calculated in combination with a physical model, empirical rules are supplemented by an expert system, results are fused through a collaborative reasoning mechanism, and weights are dynamically adjusted, so that the evaluation accuracy and adaptability are improved; potential risks are accurately identified, and reliable support is provided for safe operation of the garage.