86results about How to "Realize accurate identification" patented technology

The invention relates to a robot standing posture recovery method and device, equipment, a storage medium and a product, and the method comprises the following steps: after the falling state of a robot is detected, collecting joint angles in real time to obtain falling posture data, and calculating contact points with the ground to determine a support part set; in combination with a preset zero moment point reference trajectory, performing inverse dynamics solution based on the set to generate a joint motion sequence, and configuring the joint motion sequence as joint position instructions to form a getting-up planning trajectory; the current output torque of each joint is compensated in real time according to the track, a driving torque instruction is obtained, each joint motor is driven to execute actions, so that the robot is recovered from the current posture to the standing posture, and the problem that the preset rigid action is often not matched with the current actual supporting state when the robot falls to the ground, and consequently the robot cannot stand up when executing the getting-up action is solved. And center-of-gravity unbalance is easy to occur.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention provides an environmental protection equipment energy consumption optimization and energy-saving regulation and control method based on multi-source data fusion, and relates to the technical field of environmental protection equipment energy-saving control, and the method comprises the steps: collecting the operation data of environmental protection equipment, carrying out the multi-scale feature extraction, recognizing the energy consumption abnormity through combining the energy efficiency curve reconstruction, and carrying out the energy-saving control of the environmental protection equipment; energy consumption sensitive parameters are extracted, energy-saving space is calculated based on load fluctuation, and an optimized parameter adjusting scheme and an optimized time sequence control sequence are generated by integrating the environment load change rate and the equipment thermal inertia coefficient. The energy consumption abnormity can be accurately identified, the energy-saving space is scientifically quantified, the intelligent optimization and dynamic regulation and control of the energy consumption of the environmental protection equipment are realized, and the operation efficiency of the equipment is obviously improved.

This invention discloses a fault handling method and apparatus based on main and distribution network coordination, belonging to the field of power system automation technology. The method includes: using a graph neural network to perform topology identification and fault location of the power grid, generating fault impact features; based on the spatial range and electrical connection relationships of the fault impact features, obtaining the switches at both ends of the faulty section of the power grid, and performing fault isolation through remote control tripping; analyzing the fault isolation information using topology impact analysis; and, combined with changes in the current topology of the power grid, generating a power grid protection strategy migration mechanism and initiating a fault recovery process to restore power supply. This invention achieves dynamic modeling of the power grid topology and accurate identification of faulty sections by using a graph neural network to embed the power grid graph and combining multi-channel time series information to generate node potential state vectors, thus improving the real-time performance and accuracy of fault location.

This invention discloses a brain-computer interface (BCI) smart zoom glasses and a visual adaptive adjustment system, relating to the field of smart glasses technology. It includes an EEG signal acquisition module that attaches to the user's head to collect signals, extracts feature vectors after noise reduction, and transmits them to a core control module; a visual environment perception module that collects target and environmental data, generates structured data packets, and transmits them synchronously; a core control module that integrates multi-source data, identifies visual intent, and decides on adjustment parameters; a smart zoom execution module that adjusts the lens focal length and provides closed-loop feedback to calibrate accuracy; a visual adaptive adjustment module that adjusts lens parameters based on the environment and fatigue state; a human-computer interaction module that supports manual operation, status display, and vibration feedback; and a data storage and update module that stores data and iteratively optimizes the model. This invention achieves accurate visual intent recognition through brain-computer interaction; personalized modeling adapts to different user needs, meeting the product development and application requirements of the wearable smart device manufacturing field.

The invention discloses a permanent magnet synchronous motor high-frequency mode identification method based on common-mode voltage injection, and the method comprises the steps: calculating a sideband common-mode voltage injection range and a sideband harmonic voltage frequency component under the injection according to a sideband harmonic voltage model; a sideband harmonic current frequency component with the maximum amplitude after injection is calculated within the common-mode voltage injection range, and an electromagnetic force wave generated by the sideband harmonic current under the condition serves as a vibration excitation source; increasing the switching frequency according to a specific step length, and carrying out a frequency sweeping experiment; and analyzing the collected data, and correcting a frequency response curve according to a high-frequency electromagnetic force calculation function to obtain modal parameters of the motor. According to the method, the common-mode voltage which has no influence on the fundamental frequency current is injected into the closed-winding permanent magnet synchronous motor, and the sideband harmonic current component with the maximum amplitude after injection is selected as the vibration excitation source for frequency sweeping, so that the identification of the high-frequency mode of the motor is effectively realized. The method can be widely applied to the field of motor mode identification.

The application discloses a variable working condition cross-component causal coupling modeling and fault diagnosis method and device, and relates to the technical field of rotating machinery fault diagnosis. The method comprises the following steps: collecting and preprocessing multi-component signals under variable rotating speed working conditions of a rotating machinery; based on multivariate Granger causal modeling, prior information and dynamic disturbance information are fused to construct a dynamic causal diagram; a double-view structure of a main view of the dynamic causal diagram and an auxiliary view of a correlation diagram is constructed, multi-scale coupling features are extracted through a multi-order spectrum filter and an attention mechanism, a symmetric InfoNCE contrast learning loss and a supervised classification loss are combined to optimize and train a model, and fault classification is realized. The device is correspondingly provided with signal acquisition, dynamic causal diagram construction, feature fusion, model training and fault prediction modules. The application improves the accuracy of fault feature representation and the robustness of diagnosis under variable working conditions, is suitable for complex industrial scenes with multi-component coupling and frequent rotating speed switching, and has strong engineering practicability.

The invention discloses a transmitter system based on multi-sensor fusion and edge AI and a self-diagnosis method, and belongs to the technical field of industrial process control instruments, and an industrial intelligent transmitter system comprises a multi-sensor unit, a signal acquisition unit, a microcontroller, an AI coprocessor and a dual-mode communication unit. The multiple sensing units are used for synchronously collecting original data; the signal acquisition unit is used for converting original data into digital signals; the microcontroller is used for processing the digital signal and carrying out environment adaptive compensation to obtain compensated sensor data; the AI coprocessor is used for performing feature extraction and fault probability reasoning on the compensated sensor data to realize real-time self-diagnosis; and the dual-mode communication unit is used for transmitting the compensated process control signal and diagnosis information in different channels. The system has the functions of environment adaptive compensation, real-time self-diagnosis and the like, and the problems of parameter drift and fault response delay in an industrial environment are remarkably improved.

The application discloses a kind of multi-variable DC fault arc adaptive detection method based on similarity measure and transfer learning, to the system output current of different time period applies wavelet packet decomposition and coefficient reconstruction processing after extraction feature, and input to state identification model to carry out multi-period fault arc judgment, to current working condition through machine learning evaluation feature similarity measure value D and model output probability distribution P joint distribution mode, according to this, the extracted feature is distinguished into unidentifiable working condition type data and identifiable working condition type data, using field adaptive strategy to the simplest feature group of unidentifiable working condition type data carries out transfer learning, to update state identification model, to realize real-time accurate detection of DC fault arc under complex and variable working condition in this way.

The invention discloses an intelligent air switch control monitoring method based on lora communication, and relates to the technical field of air switch control monitoring, and the method comprises the following steps: carrying out the continuous collection of a voltage sampling sequence and a current sampling sequence of an air switch, generating a variable-amplitude density curve, a reversal interval period value and a waveform pole delay track, forming a continuous feature sequence, and carrying out the inversion of the interval period value and the waveform pole delay track; whether the sampling signal has regular distortion under the influence of the electromagnetic interference is judged; and under the condition that the sampling signal has regular distortion, extracting a distortion confirmation point set from the continuous feature sequence, and calculating a feature deviation factor, a trend residual vector and a stable window convergence degree based on each distortion confirmation point to form an abnormal form mapping group. According to the invention, the problem that the structural distortion of the sampled data under electromagnetic interference is difficult to identify is solved, distortion perception and state closed-loop regulation and control based on the feature sequence are realized, and the monitoring accuracy and the system safety are improved.

The invention relates to the field of data transmission, in particular to an internet of things data adaptive compression method and system based on edge computing, and the method comprises the steps: obtaining a historical data set and a to-be-compressed data set, calculating the correlation degree of a target dimension and a contrast dimension, segmenting the to-be-compressed data set, and clustering all segments; calculating the data anomaly probability of the target dimension in the target cluster; calculating a data adaptive compression deviation value of the target dimension in the target segment; calculating an upper door slope and a lower door slope of a target point in a target dimension based on a revolving door algorithm, calculating an upper door effective slope of the target point in the target dimension, similarly obtaining a lower door effective slope, obtaining a compression starting point of the target dimension based on the upper door effective slope and the lower door effective slope, and similarly obtaining a compression ending point; and compressing the data of the target dimension. According to the technical scheme, compression resource consumption can be reduced, and compression efficiency can be improved.

The application discloses a health-care space environment regulation method and system based on the Internet of Things, relates to the technical field of intelligent environment regulation of the Internet of Things, and comprises the following steps: collecting health-care space environment parameters and target crowd physiological parameters, constructing an environment data change vector field, forming a physiological state evolution curve, and extracting a physiological sensitive direction set; determining a comfortable environment distribution area according to a time period during which the physiological parameters are in a healthy range; searching for a candidate regulation path leading to the comfortable environment distribution area along the physiological sensitive direction, and screening a stable regulation vector in combination with accumulated energy consumption; generating a segmented regulation sequence according to an environment propagation rate, and converting the segmented regulation sequence into environment regulation equipment execution parameters for control. The application can improve the stability, comfort and energy consumption control capability of health-care space environment regulation.

This invention provides a six-legged octopus robot device for underwater operations, relating to the field of underwater robot technology. The device comprises a main body, main tentacles, secondary tentacles, and a 360° rotating gimbal. The main tentacles integrate highly elastic TPU flexible tentacles and a miniature camera, while the secondary tentacles have built-in friction wheels for current-resistant anchoring. All components are detachably connected via standardized interfaces, making it suitable for efficient inspection and modular maintenance in complex underwater terrain. It solves the problems of traditional rigid robotic arms, such as difficulty in fitting into confined spaces, poor current-resistant stability, and high maintenance costs.

The application relates to the technical field of network security, in particular to a high-risk operation instruction identification method and device and a storage medium, which comprises the following steps: acquiring keystroke coordinate records. According to the effective coordinates and the time information of the keystroke operation, the column distribution dispersion degree used for representing the horizontal direction distribution width of the keystroke operation on a monitoring target row is calculated, and the active point filling rate used for representing the horizontal direction continuity degree of the keystroke operation on the monitoring target row is calculated. Whether a command input intention exists on the monitoring target row is judged according to the column distribution dispersion degree and the active point filling rate. In response to the judgment result that the command input intention exists, the instruction sequence executed by the user final intention is reconstructed according to the keystroke character, the effective coordinates and the time information of the keystroke operation. The application provides a method for identifying the real command construction intention in a full-screen program.

The invention discloses a Hamiltonian Monte Carlo-based stacked piezoelectric ceramic transducer signal compensation device and an implementation method, and belongs to the technical field of underwater acoustic communication. The device comprises an interference mechanism modeling module, a data acquisition module, a parameter identification module and a signal compensation module. The interference mechanism modeling module is used for constructing a high-order equivalent circuit model comprising a static capacitance branch, a dynamic mechanical branch and a parasitic coupling term, and quantizing a physical interference mechanism between piezoelectric ceramic pieces with different resonant frequencies in a pilot frequency stack structure by using a corrected mutual interference admittance branch; the method comprises the following steps: acquiring voltage, current and phase difference signals of a transducer end in a low-power linear working area by using a data acquisition module, and obtaining actually measured admittance spectrum data containing inter-chip interference characteristics; the parameter identification module constructs a potential energy objective function based on a Hamiltonian Monte Carlo algorithm, and executes global sampling inference in a parameter space and outputs an optimal parameter set by calculating an interference coefficient gradient; and the signal compensation module calculates the real admittance of the main resonant piece by using the optimal parameter, and generates a time domain digital compensation signal. According to the stacked piezoelectric ceramic transducer, the influence of inter-chip complex action on acoustic signal output is effectively analyzed, and the response flatness and fidelity of the stacked piezoelectric ceramic transducer under broadband excitation are remarkably improved.

The invention relates to a building material intelligent storage inventory and fireproof early warning method and system based on computer vision and RFID, and the method comprises the steps: carrying out the building material collaborative inventory and fireproof collaborative early warning in parallel through deploying an image collection module and an RFID read-write network; in the checking process, visual target detection is utilized to position building materials and guide an RFID reader-writer to carry out directional beam scanning, so that accurate binding of visual features and RFID identity information is realized, and a high-precision inventory list is generated; in the early warning process, a temperature abnormal area is identified based on thermal imaging, visual analysis is triggered to confirm building material accumulation and open fire characteristics, and then graded fire early warning is generated. Through multi-modal data fusion and cooperative processing, the automation precision of warehouse inventory and the reliability of fire early warning are improved, and integrated intelligent operation and maintenance of inventory management and safety monitoring are realized.

The invention relates to the technical field of data security, in particular to an intelligent data security detection system based on semantic recognition, and the system is configured to execute the steps of an intelligent data security detection method based on semantic recognition: obtaining to-be-detected data from a detected system and recording and collecting metadata; performing rapid pre-filtering and preliminary labeling on the original data by applying a rule engine based on a mode, a regular expression and a black / white list; and calling a semantic recognition model and a rule engine to carry out parallel detection on the preprocessed data, and carrying out comprehensive evaluation on the model confidence, the rule matching result and the statistical abnormality based on a configurable fusion strategy. According to the method, through cooperative work of rule pre-filtering and a semantic recognition model, the detection real-time performance is guaranteed, and meanwhile accurate recognition of potential sensitive semantics in data is achieved; and comprehensive evaluation is performed by fusing the rule matching result, the semantic confidence and the abnormal features, so that the accuracy and reliability of risk judgment are improved.

The invention provides a strabismus screening system based on multi-dimensional dynamic eye movement tracking. The strabismus screening system comprises a data acquisition module, a data preprocessing module, a data analysis module and a result feedback module. A cartoon experimental normal form is adopted for an experiment through a domestic Pupil.IO AIO eye tracker, so that the eye movement track is monitored, the stability of a fixation point is dynamically quantified, the dynamic change during jump reaction is captured, multi-dimensional eye movement feature calculation such as sight line difference analysis is carried out, and finally dichotomy result judgment is carried out. The problems of inherent limitation, high equipment price, insufficient clinical suitability of a data analysis model and lack of a standardized process of a traditional inspection method are effectively solved.

This invention relates to the field of fault early warning technology, and provides a method and system for early warning of faults in large oil-filled equipment, comprising: Step 1, collecting acoustic fingerprint data throughout the entire lifecycle of the large oil-filled equipment and constructing an acoustic fingerprint sample set; Step 2, preprocessing the acoustic fingerprint sample set, extracting acoustic fingerprint feature parameters, and establishing a standardized acoustic fingerprint database; Step 3, constructing a multi-channel deep learning acoustic fingerprint recognition model and a discharge severity assessment model based on deep learning; Step 4, based on the output of the trained acoustic fingerprint recognition model and combined with a preset multi-level early warning threshold system, performing real-time assessment of the operating status of the large oil-filled equipment, and generating corresponding early warning information when the assessment result meets the early warning triggering conditions; Step 5, pushing the early warning information to the operation and maintenance terminal, and automatically generating operation and maintenance suggestions based on the defect type and severity. This invention can effectively provide early warning of faults in large oil-filled equipment.

The invention discloses a rockburst disaster prediction method based on an optimized extreme gradient lifting classification model, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining multi-shift monitoring data of a coal mine working face, carrying out the preprocessing, dividing the data into a training set and a test set, and taking an initial hyper-parameter extreme gradient lifting classifier as a basic model, and using at least one meta-heuristic optimization algorithm to optimize the hyper-parameters, training and constructing a hybrid prediction model, inputting real-time monitoring data into the hybrid prediction model to carry out rockburst disaster prediction, and outputting a coal mine rock burst disaster risk prediction result. According to the method, the technical problems of low disaster prediction accuracy and dangerous state recall rate and high missing report risk caused by insufficient sensitivity of an existing coal mine rock burst disaster prediction model to unbalanced monitoring data are solved, and the purposes of optimizing an extreme gradient lifting classifier through a meta-heuristic algorithm and improving the prediction accuracy of the coal mine rock burst disaster prediction model are achieved. The disaster prediction accuracy and the dangerous state recall rate are improved, and the missing report risk is reduced.

The application discloses an energy big data management method and system based on digital twinning, and relates to the field of energy big data management; the method comprises the following steps: collecting vibration data in real time, decomposing the vibration data into multiple frequency scale components, removing background noise signals to obtain multiple denoising frequency scale components, searching for a scale component where a main bearing wear vibration frequency is located, extracting a wear vibration energy feature of the wear vibration frequency from the scale component and calculating a confidence degree of the wear vibration energy feature, performing enhancement processing on the wear vibration energy feature when the confidence degree exceeds a main bearing wear degradation signal threshold, fusing the multiple denoising frequency scale components and the processed wear enhancement feature signal into refined data and transmitting the refined data to a digital twinning platform, and enabling the digital twinning platform to adaptively adjust a main bearing of a wind driven generator. The application can effectively identify and enhance early wear degradation signals of the main bearing of the wind driven generator, avoid misjudgment, provide more accurate refined data for the digital twinning platform, and realize adaptive adjustment on the main bearing.

This invention relates to a method for synchronous detection of road surface anomalies based on hyperspectral imaging, belonging to the field of hyperspectral image processing and remote sensing detection technology. It solves the problems of low data processing efficiency, limited detection functions, and insufficient real-time performance in existing technologies. The method includes: acquiring hyperspectral images of various road surfaces, extracting key features, generating hyperspectral fingerprints, labeling road surface categories, and constructing a hyperspectral fingerprint database; training a fingerprint comparison model based on the hyperspectral fingerprint database to obtain a pre-trained fingerprint comparison model; acquiring road hyperspectral images in real time, extracting key features, generating a hyperspectral fingerprint to be tested, and obtaining the road surface category of the hyperspectral fingerprint to be tested based on the hyperspectral fingerprint database, the pre-trained fingerprint comparison model, and a hierarchical comparison strategy. This enables simultaneous detection of multiple anomalies such as icing, water accumulation, and road surface deterioration on an UAV-borne terminal.

The application discloses an e-commerce warehouse center safety monitoring method based on deep learning, and relates to the technical field of warehouse safety vision, comprising the following steps: carrying out space-time alignment fusion on structured scene data and laser point cloud data, obtaining a three-dimensional point cloud representation, carrying out point-level segmentation and high-risk area identification on the three-dimensional point cloud representation through a PointNet network, and obtaining a warehouse high-risk area; abstracting scene objects in the structured scene data into graph nodes, establishing an interaction relationship graph according to the three-dimensional space representation, analyzing the interaction relationship between the scene objects in combination with a space risk result, generating an interaction risk result and a three-dimensional position marker; generating a three-dimensional visual picture according to the interaction risk result and the three-dimensional position marker, labeling a dangerous area and a risk object, and forming a warehouse risk visual report. The application significantly improves the automation, refinement and visualization level of warehouse safety monitoring.

The invention discloses a document processing method and device, computer equipment and a storage medium, relates to the field of document processing, and is used for solving the problem that target information is mistakenly extracted and missed to be extracted due to template mismatching when an existing document processing scheme is used for multi-version and multi-format documents. The method comprises the following steps: receiving a document processing request; generating an extraction instruction based on the user instruction and a preset prompt template, and determining multi-dimensional feature information of the document; inputting the multi-dimensional feature information into a preset multi-modal large model to obtain a document processing strategy; analyzing the document according to the analysis rule to obtain a first document; according to a preset extraction strategy, target information corresponding to the extraction instruction is extracted from the first document, and the target information is converted into structured data to be output.

The application discloses a machine tool machining anomaly diagnosis method, system, device and medium based on LOF, and relates to the technical field of machine tool machining monitoring. The method comprises the following steps: collecting position information, program instruction information and vibration signals of each axis of a machine tool; constructing a mapping unit sequence; constructing a density distribution model based on a local outlier factor algorithm; calculating a local outlier factor of a to-be-tested data point; selecting the to-be-tested data point based on the local outlier factor, and obtaining corresponding position information and program instruction information. The application does not need to manually set a fixed threshold value, does not need to rely on fault samples, realizes spatiotemporal fusion of multi-source data and accurate identification of machining anomalies, can accurately locate the machining position and program instruction corresponding to the anomaly, solves the problems that existing diagnosis methods have poor adaptability, are difficult to land, and anomalies are difficult to trace, guarantees precision machining quality, and adapts to machining requirements in the high-end manufacturing field.

This invention relates to the field of remote sensing water quality identification technology, and in particular to a method for identifying black and odorous water bodies based on a temporal convolutional network. By constructing a black and odorous water body identification model comprising a cascaded temporal convolutional network residual module, a channel attention module, a temporal attention module, a global average pooling layer, and a regressor module, and replacing the original output layer of the temporal convolutional network with the regressor module, a mapping from remote sensing image data to continuous water quality parameter predictions is achieved. This solves the technical problem of limited accuracy in identifying black and odorous water bodies by relying solely on general spectral features in existing technologies. Furthermore, by performing addition, subtraction, multiplication, and division operations on the original spectral bands to generate combined bands, and using the Pearson correlation coefficient method to select the original and combined bands with the highest correlation, a sample dataset is constructed by combining this with ground-based measured data. This further expands the sample size based on existing samples, significantly improving the model's learning efficiency.

The invention discloses a smart home control method, device and equipment, a medium and a product. The method comprises the following steps: acquiring multi-modal sensing data, and converting the multi-modal sensing data into a discrete feature descriptor sequence; constructing first prompt information according to the situation state definition information and the discrete feature descriptor sequence, and guiding a micro language model to generate a current situation state based on the first prompt information; and generating a control instruction sequence corresponding to the controlled household equipment according to the current situation state, and controlling the controlled household equipment to execute corresponding operation according to the control instruction sequence. By introducing a hierarchical context awareness mechanism, conversion from an original perception signal to a discrete semantic state is completed by using low-power-consumption signal processing and a miniature semantic model, and accurate recognition of a complex life situation under a low-computing-power condition is realized by using a zero-sample logical reasoning capability of the miniature semantic model. And the energy efficiency ratio and the response speed of edge calculation are improved.

This invention relates to the field of defect identification technology, and more particularly to an image positioning and recognition system based on UAV inspection of wind turbine blades. The system includes an image positioning and recognition center, a data acquisition module, a detection preparation module, a flight control module, an attitude dynamic management module, a detection data module, a defect type identification module, a positioning visualization module, and an operation display module. This invention uses image recognition and blade sway amplitude for dual verification, initiating detection only when there is no impact or only a slight impact and the sway amplitude meets the standard, ensuring the rationality of the detection initiation conditions. During detection, analysis is performed from two dimensions: hovering position and hovering attitude, avoiding abnormal detection data caused by UAV position deviation or attitude anomalies. After screening out effective defect areas, accurate defect type identification is achieved. Furthermore, through coordinate transformation, an intuitive 3D model of the defective blade is generated, providing precise guidance for maintenance work and significantly reducing maintenance troubleshooting costs.

The invention provides a floating cutter head control method and device, garden operation equipment and a storage medium, and relates to the technical field of garden machinery control. According to the method, an original current signal and a real-time rotating speed signal of a cutterhead driving motor are obtained, a load index is calculated by integrating the information of the two signals, accurate recognition of a real load state is achieved, and a lifting motor is controlled to automatically adjust the height of a cutterhead upwards or downwards according to whether the load index meets a high-load or low-load condition or not. A bidirectional self-adaptive adjusting mechanism of heavy-load obstacle avoidance and light-load reset is formed, so that the risk of motor stalling is avoided, the phenomena of missing cutting and repeated cutting are reduced, the continuity, uniformity and overall quality of mowing operation are improved, and meanwhile, the set mowing height is intelligently recovered, so that the attractiveness of lawn mowing and the consistency expected by a user are guaranteed.

The application provides a shield tunnel construction safety risk prevention and control method, and relates to the technical field of tunnel construction, comprising the following steps: extracting risk factors from construction data by persistent homology technology, and obtaining corresponding risk coefficients; constructing a causal network based on historical risk information and prevention and control measures, inputting the construction data and the risk factors into the causal network, and obtaining the activation probability of the risk factors and the corresponding prevention and control measures; dividing the tunnel along the line into a cell grid, simulating the risk diffusion process, and obtaining first risk information; obtaining the minimum activation probability of the risk factors after implementing the prevention and control measures based on the causal network, simulating the risk diffusion process based on the minimum activation probability, and obtaining second risk information; and recommending the prevention and control measures by comparing the first risk information and the second risk information. The application realizes accurate identification, dynamic evaluation and scientific prevention and control of the shield tunnel construction risk, effectively reduces the construction risk, and ensures construction safety.