338results about How to "Achieve real-time" patented technology

The invention relates to the technical field of power distribution automation and intelligent power grid equipment, in particular to an FTU-based intelligent voltage regulation protection bypass cooperative control system, which comprises a state acquisition module, a state identification module, a self-adaptive disturbance judgment module, a voltage regulation cooperative module and a state reconstruction module, the system establishes an operation state vector model by synchronously sampling the voltage and current of a distribution line and the states of a circuit breaker and a bypass switch; judging line operation conditions according to vector state classification, and generating corresponding action priorities and time windows; unified scheduling and interlocking control are carried out on the voltage regulation unit, the protection unit and the bypass unit through the cooperative control execution module, and logic cooperation and state closed-loop updating of multiple control objects are achieved; according to the invention, time sequence coordination and intelligent linkage of voltage regulation, protection and bypass operation can be realized under complex working conditions, the voltage stability and the operation reliability of the distribution line are improved, the fault conflict probability is reduced, and the operation and maintenance intervention is reduced.

The invention discloses a broadband spectrum compression detection method based on digital twinning and signal noise, and relates to the technical field of mobile communication, and the method comprises the following steps: S1, inputting historical spectrum data, geographic position information and electromagnetic environment parameters into a digital twinning model based on an improved FEDform model, and obtaining a digital twinning model; outputting predicted power spectral density and signal support set probability distribution; s2, generating an optimized observation matrix and a digital compression measurement vector; s3, outputting a whitening measurement value; s4, innovative components are extracted; s5, outputting reconstructed broadband spectrum data; s6, outputting a binary support set matrix containing frequency point occupancy state information; and S7, outputting the updated digital twin model. According to the invention, the limitations of dependence on high-speed sampling hardware, poor anti-noise capability and early warning delay in a traditional broadband spectrum detection method are overcome, and an efficient and accurate solution is provided.

The invention discloses a special first-aid medicine and article storage and distribution construction system and method. The system comprises an Internet of Things module, a multi-subject cooperation module, an inter-hospital cooperation module, a cloud computing platform module, a block chain module, an intelligent scheduling center module, a storage network module and an emergency response module. The method comprises the steps of 1, demand integration and joint purchase; step 2, storage network construction and inventory optimization; step 3, intelligent scheduling and path planning; 4, emergency response and cross-mechanism deployment are carried out; 5, whole-process tracing and data uploading are carried out; step 6, performance monitoring and continuous optimization; according to the invention, a special first-aid medicine and article storage and distribution construction system is designed, and standardized data interaction and close cooperation among medical institutions, suppliers and logistics enterprises are realized by establishing a multi-subject cooperation mechanism such as joint purchase, inventory sharing, emergency allocation and performance monitoring. The joint purchasing module gathers the purchasing demands of the medical institutions in the region.

The invention relates to the technical field of information management, in particular to a material digital management system and method based on a cloud platform. The method comprises the following steps: acquiring material data; distributing a material storage space by using the material data; receiving an operation message of the storage space, and pushing the operation message to an inventory database; creating a circulation record table and an inventory state table; using the operation message in the inventory database to update a circulation record table, and recording circulation data; automatically updating an inventory state table according to the circulation data so as to determine the material inventory capacity; when the material inventory capacity is lower than or equal to a preset safety inventory threshold value, management early warning information is generated; and when the material inventory capacity is higher than a preset safety inventory threshold value, updating the management safety field of the inventory state table. The material inventory is monitored in real time based on the information management technology, the replenishment information is intelligently generated, inventory dynamic management is achieved, and the material management efficiency is improved.

The invention provides a lightweight monitoring and rapid evaluation analysis method and system for small and medium span highway bridges. Comprising a bridge parameterization rapid input module, a vehicle load batch input module, a measuring point information arrangement module, a finite element calculation and analysis module, a batch load working condition measuring point result automatic extraction module and a cloud highway bridge structure response monitoring module. The method has the following advantages: 1, on-site appearance quality acquisition, material performance test, dynamic characteristic test and other work of the bridge are not needed, the safe traffic coefficient of the bridge can be evaluated in real time only by monitoring the dynamic deflection of the measuring point at the key section position based on the standardized structural characteristics of the small and medium span bridge, and the real-time performance of the evaluation result is realized; and 2, under any traffic flow condition, real-time dynamic verification coefficients of various types of overweight vehicles under different driving conditions and bridges can be accurately calculated, and timely safety early warning is realized, so that expenditures of a large amount of manpower, material resources and the like required by a load test are saved, and the cost is greatly reduced.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle target classification matching strike control method and system based on deep learning, and the method comprises the following steps: S1, collecting the original environment data of an unmanned aerial vehicle flight region, carrying out the preprocessing of the original environment data, and generating multi-mode perception data; and S2, inputting the multi-modal sensing data into a pre-constructed deep learning classification network, extracting multi-level depth features of the target through the deep learning classification network, carrying out classification identification on the target based on the multi-level depth features, and outputting target category information and target position information of the target. The multi-modal sensing data is input into the deep learning classification network for target recognition, the multi-modal fusion sensing mode can give full play to the complementary advantages of different sensors, high target recognition accuracy can still be kept in complex environments such as night, low illumination and severe weather, and the adaptive capacity of the system to environment changes is remarkably improved.

This invention discloses a distributed consistency control method and device for primary-distribution-microgrid collaborative energy storage, relating to the field of power system energy storage control technology. The method includes the following steps: acquiring the SOC deviation and voltage deviation of each energy storage node; converting the voltage deviation into a dynamic feedback gain through nonlinear mapping; performing asymmetric adaptive correction of the SOC deviation using the difference in dynamic feedback gains between adjacent nodes; generating a local active power regulation quantity based on the corrected SOC deviation and upper-level scheduling instructions; controlling the local energy storage converter to perform power regulation through the active power regulation quantity, updating the SOC and voltage of the local node, and using the updated SOC and voltage as the initial values ​​for the next control cycle. This invention achieves spontaneous transfer of physical stress in a microgrid group and self-organized survival of the system without increasing the communication bandwidth of the centralized controller.

The invention relates to the technical field of data management, and discloses a medical data secure transmission method and a big data digital signature system, and the method comprises a hierarchical processing step, a privacy enhancement step, a key negotiation step, a segmented transmission step and an audit tracking step. The system comprises a signature preprocessing module, a hierarchical signature module, a privacy purification module, a verification module and a tracing module. According to the method, a risk association map is constructed through hierarchical processing, and accurate identification of data association risks is realized; privacy and availability are balanced through differentiated privacy enhancement, authorization controllability is enhanced in combination with block chain key management, large file transmission integrity and non-repudiation are guaranteed, and whole-course audit is controllable. According to the system, the relevance between the signature and the privacy feature is improved through module design, the integrity and relevance of the signature chain are guaranteed, privacy protection and verifiability are compatible, efficient verification is achieved, the dispute traceability is provided, and pediatric medical data safety is guaranteed in cooperation with the method.

This invention discloses a method and system for monitoring the state of hybrid energy storage batteries based on big data, belonging to the field of battery state monitoring technology. The method includes: collecting battery state data and preprocessing the data; obtaining a high-dimensional feature vector based on the preprocessed data through dynamic weight adjustment and fusion; constructing a battery state prediction model based on a long short-time memory network model; obtaining a battery health value based on the high-dimensional feature vector through the battery state prediction model; and monitoring the battery health state in real time based on the battery health value. This invention significantly improves the accuracy of battery health value prediction by dynamically adjusting weights and fusing to generate a high-dimensional feature vector, achieving real-time monitoring and fault early warning of battery health state under complex operating conditions, and effectively improving the safety, reliability, and operational management efficiency of hybrid energy storage systems.

The invention provides a method for reducing the failure rate of a unit air cooling island speed reducer. The method comprises the following steps that S1, a multi-module cooperative prevention and control system is built; s2, configuring a parameter monitoring module assembly; s3, standardizing the installation standard of the sensor; s4, building a data transmission and analysis platform; s5, starting an intelligent diagnosis and data tracing function; s6, executing a monitor calibration and maintenance process; s7, carrying out preventive maintenance operation; s8, starting a working condition linkage adjusting mechanism; s9, activating an operation and maintenance training auxiliary module; and S10, a supplement lubricating oil leakage monitoring link. According to the method for reducing the failure rate of the unit air cooling island speed reducer, full-dimension real-time monitoring and intelligent diagnosis of operation parameters of the speed reducer are achieved by building a multi-module cooperative prevention and control system, and by combining graded early warning and preventive maintenance, fault hidden dangers are accurately positioned and handled; the working condition linkage adjusting mechanism can actively adjust the operation state when parameters are abnormal, and fault expansion is avoided.

The present invention relates to the field of die-casting optimization technology, and discloses a die-casting process parameter optimization method and system based on digital twin. The method includes: arranging sensors to collect die-casting equipment operation parameters and die-casting part quality data in real time to form multi-source die-casting production data. According to the multi-source die-casting production data, establish a multi-physics field simulation model and construct a digital twin model. Compare the virtual prediction results with the measured quality data, and construct a virtual-real difference compensation network to correct the parameters of the digital twin model. Based on the compensated digital twin model, perform a multi-objective reinforcement learning method to generate the optimal die-casting process parameters. Apply the optimal die-casting process parameters to the die-casting equipment for verification, and update the virtual-real difference compensation network according to the verification results. This application realizes the intelligent optimization and continuous self-evolution of die-casting process parameters, and improves the casting forming accuracy, energy efficiency utilization rate and production stability.

This application provides a motion control method and system for an automated battery electrolyte filling process, comprising: acquiring the position information of each joint of a robotic arm; calculating the instantaneous position and orientation of the filling needle in three-dimensional space based on the position information of each joint and the kinematic model of the robotic arm; comparing the calculated instantaneous position and orientation of the filling needle with a preset ideal motion trajectory of the filling needle; determining the deviation between the actual spatial state and the ideal spatial state of the filling needle based on the comparison result; generating compensation commands for adjusting the motion of each joint of the robotic arm based on the deviation; and sending the compensation commands to the servo driver of the robotic arm to adjust the motion of each joint of the robotic arm. This application can improve the accuracy of battery electrolyte filling and product yield.

The invention relates to the technical field of energy scheduling of an energy storage system, and provides a partition control energy scheduling method and system for an energy storage system in a holographic data scene, which realize synchronous acquisition and fusion of an internal battery state and external environment data to form a complete system operation portrait. And further, a multi-parameter SOE deviation dynamic model including charging and discharging behaviors, capacity attenuation trend and thermal influence is established, so that the system can sense energy deviation of each partitioned energy storage unit in real time. On the basis, a partition cooperative control decision model with the minimum SOE deviation as a target function is constructed, multiple constraints such as the operation capacity of the energy storage converter, the power grid requirement and the safety limitation are integrated, the model is solved through a Lagrange multiplier method, and initial power distribution of all partitions is obtained. And finally, correcting an SOE prediction error by means of high-frequency real-time input of holographic data, and adjusting charging and discharging power distribution in combination with SOE dynamic deviation to realize refined optimization of a partition scheduling scheme.

This invention belongs to the field of conveying equipment technology, specifically disclosing a conveying device and method for egg supply, including a conveyor belt machine, a chamber, and a blower. The conveyor belt machine's external belt consists of three sets of circular tracks that wrap around the conveyor belt machine. The circular tracks have notches spaced evenly along their circular paths. The chamber is fixedly installed above the conveyor belt machine and near its center. A ventilation component is installed above the chamber. A U-shaped frame is fixedly mounted above the conveyor belt machine and below the ventilation component. A crack visual detection marking component is installed above the U-shaped frame and corresponding to each circular track. This invention integrates wiping, flipping, drying, visual detection, and marking functions, enabling online identification and marking of cracked eggs produced after cleaning. This effectively prevents cracked eggs from being mixed with qualified products, significantly improving the overall quality and automation level of egg supply.

The application discloses a data search and classification method based on eigenvalue index, comprising the following steps: obtaining a set based on original data through a regular semantic hybrid parser, wherein the set comprises feature names and feature values; obtaining a classification vector based on the set through mapping; obtaining classified index data based on the classification vector through two-level index search of multidimensional reverse arrangement and bitmap; obtaining a query data subset based on user query conditions through query rewriting, feature sorting and bitmap intersection; obtaining an updated query data subset based on the query data subset through sequential scanning and sorting; and obtaining updated classified index data based on the query data subset and new data through a bitmap merging strategy.

The application discloses an emotion recognition method based on online cross-modal knowledge distillation, comprising the following steps: acquiring electroencephalogram and electrocardiogram original signals and windowing and cutting; constructing electroencephalogram and electrocardiogram student models, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; constructing a teacher probability distribution through a joint encoder fusion; introducing adaptive contrast loss to align the cross-modal intermediate features, introducing distillation loss to constrain the prediction probability distribution of each modal to align with the teacher probability distribution; synchronously optimizing new student model parameters through online collaborative training; and performing actual inference prediction based on the student model after training. The application combines double modal signals to make up for the defects of single modal information, excavates the complementarity of modes, realizes dynamic generation of teacher supervision signals and real-time collaborative learning of modes through online distillation, does not increase test calculation overhead, effectively improves the recognition accuracy, model robustness and generalization ability, and has good application prospect.

This invention relates to the field of industrial Internet of Things (IoT) technology, and more particularly to an interactive system for tracking and providing feedback on maintenance strategies and tasks. The system includes a data hub and fusion module, a health assessment and early warning module, a maintenance strategy intelligent generation module, a task tracking and execution feedback module, a feedback optimization and knowledge base module, and a visualization push module. By constructing a multi-module collaborative intelligent maintenance platform, this invention achieves closed-loop management of the entire process, including equipment status monitoring, health assessment, maintenance decision-making, task execution, and knowledge optimization. It solves the problems of traditional systems relying on fixed rules and lacking continuous improvement; it also addresses the issues of low information transmission efficiency and insufficient decision support, ultimately achieving comprehensive optimization of equipment reliability, maintenance costs, and operational management efficiency.

The present application belongs to the technical field of port safety monitoring, and specifically relates to a method and system for identifying personnel approaching a cable launching area in a port, a terminal and a storage medium, which comprises selecting a fixed cable column of a port berth as a physical reference, using a high-resolution network camera to collect panoramic images of the port berth, using a pre-trained YOLOv8 deep learning model for multi-target segmentation, and performing secondary focusing identification to quickly and accurately identify personnel, cables, cable columns and ship outline targets, and by obtaining dynamic factors such as cable tension and ship docking angle, the reference radius of the dangerous area is corrected by combining the corresponding calculation formula, and a dynamic dangerous area polygon is generated, fully considering the dynamic changes of the port operation environment, which can update the dangerous area in real time, making the division of the dangerous area more in line with the actual situation, avoiding misjudgment and missed judgment due to the fixed dangerous area, and improving the accuracy and effectiveness of safety management.

The invention relates to a radiation meter dense feed source array directional diagram optimization method based on a generative adversarial network. The method comprises the following steps: constructing a training data set, a condition vector containing a target directional diagram performance index and a corresponding optimal amplitude-phase weight vector; designing a conditional generative adversarial network model; constructing a physical constraint loss function by embedding a differentiable electromagnetic calculation module; loading the constructed training data set, and training a complete generative adversarial network model based on a loss function embedded with a differentiable electromagnetic calculation module; and when the system antenna pattern obtained according to the amplitude-phase adjustment parameters output by the generative adversarial network model meets the antenna pattern evaluation index, completing dense feed source array antenna pattern optimization. According to the method, the target weight is dynamically adjusted and optimized on orbit, so that a single model can adapt to beam forming requirements in different observation scenes, and flexible switching of multiple task modes such as high-resolution detection and wide-range general survey is realized.

The utility model discloses an emergency defoaming device for a nuclear power plant. The emergency defoaming device comprises an execution system, a control system and a power supply system, according to the emergency defoaming device for the nuclear power plant, the main flow pipeline, the branch flow pipeline and the spraying assembly are arranged, so that one solvent tank can be used for filling a plurality of defoaming pools at the same time. Meanwhile, the video monitoring module is utilized, the flow and concentration of defoaming spraying can be automatically adjusted according to the foam amount collected by the video monitoring module, high efficiency is achieved, the operation cost is reduced, the function of intelligent decision control can be achieved through the arrangement of the data processing module, and the purpose of unmanned defoaming is achieved. And a remote control module and a wireless transmission module are also arranged, so that the functions of real-time monitoring and remote operation are realized, and the cost and risk of manual intervention are reduced. In addition, a generator, a photovoltaic panel and an energy storage device are further arranged, traditional mains supply is combined with new energy power supply of wind energy and solar energy, and redundancy and reliability of power supply are achieved.

The application is suitable for the technical field of shield engineering data processing, and provides a multi-modal data fusion slag discharge quantity monitoring and early warning method, device and terminal equipment. The method comprises the following steps: acquiring first slag discharge quantities of each tunneling ring measured by a shield machine belt weighing system, weight time series data of a gantry crane during the process of hoisting and transporting slag, and preset upper and lower limit thresholds of the slag discharge quantity; determining actual and empty bucket weights of each slag bucket corresponding to each tunneling ring according to the weight time series data; determining a second slag discharge quantity of each tunneling ring according to the actual and empty bucket weights of each slag bucket corresponding to each tunneling ring; and monitoring and early warning the shield machine slag discharge quantity according to the difference between the first slag discharge quantity and the preset upper and lower limit thresholds of the slag discharge quantity, the difference between the first slag discharge quantity and the average of the slag discharge quantities of the previous N tunneling rings, and the difference between the first slag discharge quantity and the second slag discharge quantity. The application can realize high-precision accounting and real-time early warning of the slag discharge quantity.

The application provides an electrically connected steam drive thickened oil steam injection optimization method and system suitable for variable working conditions, and belongs to the field of thickened oil thermal recovery. The method comprises the following steps: constructing a partition variable working condition thermodynamic model of an electric heating furnace, a magnesium brick heat storage device dynamic heat storage and release model, a heat exchange steam production unit thermodynamic model and a heat transfer model along an injection wellbore; using a multiple variable working condition identification method facing the oil reservoir side, the energy side and the pipe network side to identify the working condition; using a model predictive control framework to perform real-time optimization on multiple targets facing multiple variable working conditions, and using a feedback correction mechanism to perform online correction on model parameters; using a scheduling optimization method facing multiple well steam distribution to distribute and schedule multiple well steam. The application can provide technical support for steam injection system operation optimization, energy cost control and power grid collaborative scheduling in the development of thickened oil steam drive, and has good engineering popularization value and application prospect.

The application discloses a kind of for the method, device, equipment and medium of tab abnormality detection in battery cell production process, comprising: the tab area image collected in battery cell production process is segmented to generate tab mask;Based on the tab mask extraction multi-scale pixel-level feature vector, and the multi-scale pixel-level feature vector is input into unsupervised probability GMM model to identify pixel-level abnormal point;The pixel-level abnormal point is analyzed to obtain candidate abnormal area, and the parameter index of the candidate abnormal area is obtained;Determine whether the parameter index meets the set tab abnormality judgment rule, if meet, it is judged as tab abnormality, and tab abnormality result is output.Therefore, the application can be widely applied to the detection of tab folding, crease, fracture, misplacement and other abnormalities on battery cell production line.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

The invention relates to the technical field of artificial olfaction and intelligent sensing, and discloses a gas recognition method and system based on a physical-data dual-drive framework. The method comprises the following steps: acquiring a macroscopic dynamic sensing signal triggered by target gas on a sensor array containing atomic-scale catalytic active sites, and a microscopic physical descriptor for representing an atomic-scale electron interaction rule between gas molecules and the catalytic active sites; and the data branch network and the physical branch network are respectively input into the data branch network and the physical branch network for feature extraction and mapping, cross-modal fusion is carried out through a feature fusion sub-module to obtain joint feature representation constrained by a physical rule, and accordingly, a gas category identification result is output. According to the method, the microcosmic physical descriptors are fused into the model reasoning logic, so that the recognition precision of the structural similar object is effectively improved.

The invention relates to the technical field of forge piece machining control, and particularly discloses an automatic control management system for vehicle forge piece machining, and the system comprises an online monitoring module which is disposed in a forging forming operation area of forge pieces and is used for carrying out the multi-dimensional real-time data collection of the forging forming process; the data processing and analyzing module is used for receiving and preprocessing the multi-dimensional real-time data, comprehensively analyzing the preprocessed multi-dimensional data and generating corresponding forging quality evaluation parameters; the state judgment module is used for comparing the quality evaluation parameters with a preset quality evaluation threshold range; real-time monitoring and self-adaptive optimization of the forging forming process are achieved, the quality threshold value and the sampling inspection frequency can be dynamically adjusted, and multi-dimensional sensing of the forging forming process, precise judgment and classification of the forging quality and self-adaptive optimization of the key threshold value are achieved.

The invention discloses a sports injury risk real-time assessment and early warning system. The system comprises an intelligent wearable device for collecting multi-dimensional physiological data and a remote analysis server for deep analysis; the server performs real-time risk assessment and graded early warning according to a personalized threshold value by fusing and analyzing the heart load, the body fluid balance state and muscle and joint stability characteristics extracted from kinematics parameters, and issues a specific intervention instruction; according to the invention, real-time perception of multiple risk factors of sports injury is realized through an end-cloud collaborative architecture, professional laboratory-level analysis ability is integrated into daily wearable equipment, the contradiction between single early warning dimension of existing equipment and incapability of real-time convenient application of a professional system is solved, accurate and prospective injury prevention guidance can be provided for a user, and the user experience is improved. And the safety of movement is greatly improved.

The application relates to the technical field of welding equipment, and discloses a self-walking welding robot, a control method and system thereof, and a storage medium, wherein the control method of the self-walking welding robot comprises the following steps: a data acquisition board acquires a welding data set in a welding process, the welding data set comprises at least two types of welding data, and the welding data set is transmitted to a touch control device through a preset communication protocol; the touch control device displays different types of welding data in corresponding display areas, receives operation instructions input by a user through the display areas, and sends the operation instructions to a PLC controller; and the PLC controller generates corresponding control signals according to the operation instructions, and controls the self-walking welding robot to perform corresponding welding actions and / or walking actions. The centralized control and man-machine interaction of various function modules of the welding robot are realized, so that the system response speed, operation intuitiveness and controllability of the welding process are improved.

This invention discloses a method for calculating the structural strength of a fiberglass tower, comprising the following steps: acquiring real-time sensor data and static design parameters of the fiberglass tower; constructing a structural digital twin model synchronously mapped to the physical entity of the fiberglass tower based on the real-time sensor data and static design parameters; injecting the real-time sensor data as boundary conditions into the digital twin model; driving the digital twin model to perform structural strength evolution analysis under multiple working conditions; and calculating the dynamic safety factor of the fiberglass tower based on the structural failure boundary and real-time sensor data. This invention also discloses a structural strength calculation system, comprising: a data acquisition module, a digital twin model construction module, a digital twin engine module, a safety assessment module, and a human-computer interaction module. This invention achieves real-time and accurate assessment of the structural strength of fiberglass towers by constructing a digital twin model, solving the technical problem that traditional calculation methods cannot dynamically reflect the structural performance degradation process.

This invention is applicable to the fields of grouting technology in mining engineering and geotechnical engineering. It provides a real-time spatial perception and tracking method for grout diffusion based on the localization of grout dynamic sound sources. The method includes: active enhancement and feature modulation of the grout dynamic sound source; construction of a stereo acoustic sensor network and high-fidelity acoustic data acquisition; AI-driven grout sound source identification and three-dimensional spatial localization; and three-dimensional real-time visualization and intelligent early warning of the grout diffusion process. This invention creatively proposes a comprehensive solution integrating active sound source enhancement modulation, stereo acoustic sensor network, artificial intelligence deep noise reduction, and three-dimensional spatial inversion localization. Its core lies in actively enhancing and feature-enhancing the originally weak and difficult-to-use grout flow sound signal to construct a high signal-to-noise ratio traceable grout dynamic sound source signal. Combined with advanced signal processing and localization algorithms, it achieves real-time three-dimensional spatial perception and visualization tracking of the grout diffusion front position and the overall diffusion range.