520 results about "Control measure" patented technology

The invention discloses a distribution cable branch box state monitoring method based on state recognition, and particularly relates to the technical field of power monitoring, and the method comprises the steps: collecting multi-dimensional operation data of a plurality of branch boxes in a continuous time period, and constructing a state evolution sequence; calculating a state offset score and an adjacent equipment state consistency score to judge whether an evaluation process is triggered or not; after triggering, constructing a state influence probability map, identifying an abnormal influence path, extracting a state disturbance diffusion index and a cooperative behavior deviation index, inputting into a pre-trained risk identification model, generating a risk level interval and a cause probability distribution vector, executing an influence regulation and control measure, and updating a state identification logic; according to the method, dynamic perception of the state of the branch box is realized by constructing a state evolution sequence, combined judgment of individual and group behaviors is realized by combining a state offset score and an adjacent equipment state consistency score, and a risk level interval and cause probability distribution vector are used for driving regulation and control strategies and map updating. And the monitoring precision and the self-adaptive capability of the system are improved.

The invention discloses a mine safety risk prevention and control method and system based on multi-source data fusion, and particularly relates to the technical field of mine safety monitoring, and the method comprises the steps: obtaining surface topography, an underground geologic structure and real-time monitoring parameters through remote sensing images, drilling data and dynamic monitoring equipment, and constructing a mining area geologic database with consistent time and space; fusing geologic feature data, integrating earth surface and underground feature data by using a three-dimensional geologic modeling technology, and mapping dynamic monitoring parameters into a model to generate a dynamic geologic feature field; performing clustering analysis on the abnormal feature points based on a spatial clustering algorithm, and calibrating a potential safety hazard region; calculating a regional risk comprehensive index by combining the risk values of the dynamic features, the geological features and the spatial features; early warning is triggered in a graded mode according to the comprehensive index, prevention and control measures are put forward, and the evaluation model is dynamically updated; comprehensive support is provided for mine potential safety hazard area monitoring and safety management decision making, and the method is suitable for risk management scenes of complex mining areas.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention discloses a tension optimization control system and method for a tensioning device of an underground coal mine belt conveyor, and relates to the technical field of tensioning device optimization control. The method comprises the steps that S1, multi-source tensioning data are collected in real time, and data preprocessing is conducted on the multi-source tensioning data; s2, predicting a coal flow load based on historical multi-source tensioning data, and taking a dynamic compensation measure for a tensioning device; s3, in combination with the multi-source tensioning data and the coal flow load prediction result, collaborative distribution is carried out on the hybrid execution mechanism, and automatic regulation and control are carried out based on the collaborative distribution result; s4, abnormity is detected in real time based on the multi-source tensioning data, and early warning is carried out; s5, performing safety risk assessment according to the real-time multi-source tensioning data, and taking adaptive regulation and control measures in combination with a safety risk assessment result and an anomaly detection result; the problem that faults are caused due to the fact that high-frequency pulsation and short-time impact of belt tension are difficult to adjust in time is solved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention discloses a dynamic flow control method for a two-phase cold plate liquid cooling system based on multi-modal sensing, and relates to the technical field of equipment cooling. Comprising the following steps: S1, collecting liquid cooling monitoring data in real time, and performing data preprocessing; s2, judging the state risk of the cold plate, carrying out signal cross validation, and taking an abnormal regulation and control measure; s3, cooling liquid flow regulation and control are carried out, and the matching degree of flow regulation and control and chip thermal load is evaluated, so that flow regulation and control correction is carried out; and S4, constructing a big data modeling and simulation feedback platform, evaluating a flow regulation and control self-healing effect, optimizing each parameter and strategy, and carrying out self-learning evolution. The problems that an existing two-phase cold plate liquid cooling system generally only depends on a single temperature signal, lacks a multi-source redundancy check mechanism, has the problems of response lag, weak local reflection capacity, high risk during failure and the like, consequently, the cooling capacity distribution and adjustment precision is insufficient, and the actual requirement of a high-heat-density data center for heat dissipation is difficult to meet are solved.

The invention provides an intelligent water and soil conservation monitoring and management system, and belongs to the technical field of hydraulic engineering and environment monitoring. The data acquisition layer acquires data in real time by using an acoustic sensor network, machine vision monitoring equipment, Internet of Things monitoring equipment, satellite remote sensing monitoring, unmanned aerial vehicle inspection and other technologies. And the data transmission layer transmits the acquired data to the data processing and analyzing layer by adopting a wireless communication network. The data processing and analysis layer analyzes multi-source data through data fusion and preprocessing, GIS technology application and big data analysis and machine learning. And the decision support and application layer comprises a prevention and control measure generation and optimization unit, a real-time early warning and emergency response unit and a user interaction and decision support unit, provides a water and soil conservation and dike reinforcement strategy for a user, and has a feedback optimization mechanism. By comprehensively applying various monitoring technologies and data analysis methods, the accuracy and timeliness of water and soil loss monitoring are improved, and powerful guarantee is provided for dike safety.

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

The invention discloses an engineering construction quality safety risk management system, and belongs to the technical field of safety risks. Comprising a project management module, a quality safety risk management and control module, a compliance review and data analysis module, an integrated project management module, a risk identification and evaluation module, a risk prevention and control strategy module, a dynamic monitoring and early warning module, an accident handling and tracing module, a compliance review auditing module and a data analysis report module. According to the invention, basic information and design parameters of construction projects are collected through the project management module, a BIM model is established, construction simulation, conflict detection and resource optimization are carried out, an actual construction plan and schedule are formulated, multivariate data in the construction process are collected in real time, early recognition, evaluation and positioning of construction safety quality risks are realized by using the model, and the construction safety quality risk is evaluated and positioned. A risk prevention and control strategy is formulated, key parameters are monitored in real time, early warning is given out immediately when data exceed a threshold value, a risk model and prevention and control measures are improved, and efficiency and safety are improved.

The invention relates to a slope structure stability safety monitoring method, which comprises the following steps of: acquiring characteristic data of geological conditions, stress states and potential instability modes of a slope, partitioning the slope according to the characteristic data, and acquiring a plurality of monitoring partitions; according to the weathering degree and structural surface development characteristics of the slope material in the monitoring subarea, obtaining a dominant deformation mode and key influence factors of the monitoring subarea; based on the dominant deformation mode and the key influence factors, obtaining a sensor optimization layout scheme in the monitoring subarea, and obtaining monitoring data of the monitoring subarea; and performing spatialization processing on the monitoring data, obtaining a spatial distribution diagram of the monitoring data, analyzing the spatial distribution diagram, identifying an abnormal region, evaluating the stability of the abnormal region, and obtaining the stable state of the slope. According to the invention, through zone monitoring and machine learning, the accuracy and pertinence of slope stability evaluation are improved, and a scientific basis is provided for making prevention and control measures and emergency plans.

The invention discloses a dynamic expansion management and control method for highway accident congestion based on an ETC portal, and relates to the technical field of traffic management and control, and the method comprises the steps: obtaining accident information; calculating the queuing length of the accident scene; the congestion duration is predicted, wherein the congestion duration is composed of rescue time, obstacle clearing time and congestion dissipation time; dilatation management and control are carried out in different areas, a control area and a buffer area are sequentially divided from the accident occurrence place along the rear, and dynamic dilatation management and control are carried out according to the actual traffic capacity of the accident road section. The method has remarkable advantages in the aspects of quick response, resource adjustment, timely rescue, accurate prediction, plan making and the like, decision scientificity is improved, effectiveness of active traffic management is improved, management strategies of active traffic management are refined, scientificity and rationality of dynamic management and control measures are improved, and the method is suitable for popularization and application. The method is of great significance in improving the emergency processing capacity of the expressway and guaranteeing traffic safety and smoothness.

The invention relates to the technical field of security management and control, in particular to an intelligent security dynamic identification management and control system, which comprises a multi-mode sensing module, a high-definition camera array, a millimeter-wave radar, an infrared sensor, an RFID reader-writer and a data processing module, and is used for receiving data of the multi-mode sensing module, carrying out time-space alignment and data fusion processing, and sending the data to the multi-mode sensing module. And personnel features, article features and environment features are extracted. The system also comprises an artificial intelligence analysis module and a user interaction module. According to the intelligent security dynamic identification management and control system, an artificial intelligence analysis module constructed by using a deep learning algorithm can perform real-time analysis and prediction on personnel behaviors, article states and abnormal events, discover potential safety hazards in advance and take corresponding management and control measures, thereby realizing conversion from passive defense to active prevention and control. For example, the system can give an alarm in time according to multi-dimensional information such as temperature change, smoke concentration and abnormal behaviors of people at the initial stage of a fire, so that precious time is won for people evacuation and fire extinguishment.

The invention relates to the technical field of power system fault monitoring, in particular to a power system fault monitoring and active control system and method.The method includes the steps that a three-dimensional model of a power distribution network in a target area is constructed, the power distribution network is divided in a refined mode, power parameters of all segments are analyzed in real time, and the operation fluctuation value of the power distribution network is calculated; the method comprises the following steps: determining whether abnormity exists or not, identifying and determining an abnormal segment when the abnormity of the power distribution network is detected, then analyzing the influence degree of the abnormal segment and carrying out grade division to evaluate the potential threat of the fault to the power distribution network, automatically matching a corresponding fault processing channel according to the grade of the influence degree of the abnormal segment, and executing corresponding control measures. Therefore, the fault source can be accurately identified, the control strategy is actively adjusted according to the actual influence condition of the fault, the influence of the fault on the power system is effectively reduced, and the stability and reliability of the system are improved.

The invention relates to the field of prediction and early warning of coal mine disasters, and discloses a risk pre-control decision support system for coal mine fire and gas disasters, which adopts hierarchical and micro-service design and integrates a data layer, a model analysis layer and a decision application layer: designing a multi-dimensional sensor network in the data layer to comprehensively monitor coal mine environmental parameters; deploying a local large model in the model analysis layer to perform disaster-causing factor feature extraction and risk prediction, and constructing a local pre-control measure knowledge base to realize protective application of privatized data; and a visual display and man-machine interaction function is provided in the decision application layer, and a user is supported to adjust a decision according to an actual situation to generate a customized emergency response scheme. According to the system, the problems of multi-modal data fusion and privatized data protection are solved, the accuracy of fire and gas risk prediction and the pertinence of decision support are improved, and support is provided for accurate pre-control of coal mine fire and gas disasters.

The invention relates to a polar region ship navigation risk management and control method and system, and the method comprises the following steps: obtaining polar region natural environment original data, predicting missing data points through a random forest, and obtaining a high-density environment risk data sample in a space-time dimension; a Bayesian network model of a polar region ship navigation process is constructed based on accident risk factors, model parameters are calculated based on environment observation data, and navigation safety assessment in a multi-risk mode is realized by constructing a risk assessment model; and for the most important risk type after navigation safety assessment, comprehensive risk management and control of polar region ship navigation are realized in combination with ship seaworthiness, ship navigation window period, air route and risk management and control measures. Compared with the prior art, the method has the advantages that environment and climate real-time characteristics are fully considered, risk assessment and comprehensive risk management and control are realized through subjective and objective combination, and the like.

The invention relates to the technical field of beehive monitoring, and particularly discloses a beehive all-generation monitoring system and method, and the system comprises a data collection module, a data transmission module, a data storage module, a data analysis module, and a user interface module. According to the method, multi-modal data fusion and deep learning are carried out by using an enhanced AI algorithm, the population number, variety and development stage of the megapes can be accurately identified, the high-precision monitoring can effectively reduce false alarm and missing alarm, the accuracy of the monitoring data is ensured, and by analyzing the environmental data and megape population dynamics in real time, the megapes can be accurately identified. The system can predict the growth trend and the potential outbreak time of a megacebee population in advance, and a scientific basis is provided for taking prevention and control measures in time; the system can dynamically adjust the monitoring frequency, the sensor power and other resource allocation according to the real-time monitoring data and the AI analysis result, unnecessary energy waste is avoided, and the overall efficiency and sustainability of the system are improved.

The invention discloses an intelligent control system for a high-precision emulsifying pot, relates to the field of intelligent control, and is used for collecting multi-dimensional data such as temperature field distribution, material rheological characteristics, liquid drop behaviors and mechanical states in real time through distributed sensors and monitoring the emulsifying process in real time. The system evaluates the phase stability risk level by adopting a multi-source data fusion technology, and dynamically adjusts the process parameters such as the stirring rotating speed, the emulsifier flow and the heating power according to the risk level. For a high-risk state, by analyzing the correlation between a temperature field and torque fluctuation, an abnormal reason is accurately identified, and a targeted regulation and control measure is triggered. A liquid drop coalescence monitoring mechanism is introduced into the system, and when liquid drop coalescence exceeds the standard, temperature compensation is automatically enhanced. The system has a self-learning capability, dynamically optimizes a risk judgment threshold according to historical data, and improves the control precision.

The invention relates to the technical field of water traffic safety supervision, and particularly discloses a water traffic safety risk grading management and control system and method, and the system comprises a data collection and processing module, a risk judgment module, and a risk grading management and control module. The data acquisition and processing module is used for acquiring and preprocessing risk data; the risk judgment module is used for carrying out risk level judgment on the preprocessed risk data to obtain a risk level judgment result; the risk grading management and control module is used for constructing a risk grading management and control measure library, and performing automatic matching of management and control measures in the risk grading management and control measure library based on a risk grade judgment result to obtain risk management and control measures; and transmitting the risk level judgment result and the risk management and control measures to different institution users. The system provides relatively objective and accurate risk level judgment rules, supports risk information level management and control, and supports collaborative linkage of the risk information among different users.

The invention discloses a liquefied natural gas (LNG) early warning system and method for dynamically monitoring an LNG ship, and the method comprises the steps: collecting the output amount, cabin pressure, environment temperature and pump operation parameters in real time, combining a multi-factor correction model to dynamically calculate the actual evaporation rate, and reversely setting an allowable evaporation rate threshold according to the maximum capability of a BOG processing system; the evaporation rate is ensured to be within a safe range through a grading alarm mechanism and safety regulation and control measures; the problem that in the prior art, static testing cannot reflect evaporation rate changes in real time is solved. Liquefied natural gas whole life cycle monitoring and performance degradation early warning of the LNG ship are achieved.

The invention provides a shale oil gathering and transportation pipeline leakage risk analysis and safety prevention and control method which comprises the following steps: establishing an FTA model to analyze a pipeline leakage accident of a shale oil gathering and transportation pipeline, and defining pipeline erosion as a key risk factor causing the pipeline leakage accident; performing quantitative risk analysis on pipeline leakage by using a Bayesian network, and calculating the probability of pipeline leakage; establishing a pipeline erosion model in Fluent software, and simulating reasons of pipeline leakage risks; establishing a pipeline leakage diffusion model of the shale oil gathering and transportation pipeline in FLACS software; performing grid division on the pipeline leakage diffusion model; setting pipeline leakage output gas components and component proportions in the pipeline leakage diffusion model; setting a pipeline leakage aperture and leakage pressure in the pipeline leakage diffusion model; setting factory environment parameters; analyzing a shale oil and gas leakage diffusion rule by using FLACS software; and formulating pipeline leakage risk safety prevention and control measures based on quantitative analysis of influence factors before and after pipeline leakage.

The invention discloses a pneumatic ash conveying intelligent control method and system based on an intelligent pilot valve, and relates to the technical field of intelligent control. According to the pneumatic ash conveying intelligent control method based on the intelligent pilot valve, when materials to be conveyed are conveyed in an ash conveying pipe, time sequence data are conveyed in real time, the time sequence data are pushed in real time, data analysis is conducted respectively, and a comprehensive conveying index of the ash conveying pipe at each time point and a pushing index of an air conveying pipe at each time point are obtained; performing comprehensive analysis to obtain a valve control index at each time point, and performing analysis to obtain a comprehensive valve control index change rate; according to the method, the comprehensive valve control index change rate and the preset comprehensive valve control index change rate interval are judged and analyzed, and the corresponding control measures are taken based on the judgment and analysis result, so that the change in the conveying process is mastered in real time, the dynamic optimization of the ash conveying process is realized, and the ash conveying efficiency is improved. And therefore, the conveying stability and the overall ash conveying efficiency are improved.

The invention discloses a quality control and real-time supervision system for pile foundation concrete, which comprises a data storage unit and a man-machine interaction terminal, and is characterized by further comprising a supply and demand matching module, a detection distribution module, a quality monitoring module and a plurality of detection units bound with unique detection numbers. Through automatic supply and demand matching of a pile foundation project and a stirring station and random distribution of the detection units, bidirectional real-time detection of delivery and entry of concrete can be realized to prevent unqualified concrete from flowing into a pile foundation construction site, and meanwhile, automatic judgment and sending of different warning information can be realized, so that the construction efficiency is improved. Therefore, different responsible subjects can be reminded to check the quality safety risk of the concrete in time and take corresponding control measures.

The invention discloses a soft rock TBM jamming risk intelligent prediction method based on numerical mechanism guidance, and the method comprises the following steps: S01, building an extrusion soft rock stratum TBM dynamic tunneling whole process refined numerical simulation model which comprises a TBM cutterhead, a front, middle and tail step shield, soft rock, a segmental lining and a backfill material behind a wall, and simulating an evolution process; s02, systematically calculating and analyzing surrounding rock geological parameters, TBM tunneling parameters, TBM construction parameters and TBM construction parameters, and constructing a machine blocking index numerical value sample library; s03, predicting the large deformation of the soft rock and the extrusion load of the shield, fusing TBM rock-machine interaction multi-source data in actual engineering, and establishing a soft rock TBM jamming risk intelligent prediction model; and S04, evaluating the TBM jamming risk, and verifying the effectiveness of the prevention and control measures. According to the technical scheme, the whole process of dynamic interaction between the TBM and the soft rock can be comprehensively considered; the TBM jamming disaster occurrence mechanism is explained, and the generalization ability of the machine learning method in practical application is improved.

The invention discloses a coal transportation and management method combined with an internet of things technology, which collects key data in a coal transportation process in real time through a multi-layer sensor network, and enhances the stability and continuity of data transmission by using edge calculation and an adaptive network technology. Meanwhile, intelligent data processing and prediction are carried out in combination with big data analysis and a machine learning model, graded early warning feedback and control measures are provided, and the transparency of transportation management is improved. Besides, the scheme also supports environmental protection monitoring and low-carbon management, data tracing and management are realized, the real-time monitoring capability, the data transmission stability, the emergency response speed and the environmental protection management level of coal transportation are remarkably improved, and the problems existing in traditional coal transportation management are effectively solved.

The invention discloses a real-time emergency prevention and control system and method for an electrochemical energy storage system based on digital twinning, and belongs to the field of energy storage safety. The system comprises a multi-source sensing unit, a digital twin deduction unit and a visual emergency decision unit. The sensing unit collects multi-source data to form a state vector; the deduction unit outputs a future thermal runaway evolution path containing a temperature field, a gas concentration field and a fire blast risk probability and uncertainty measurement thereof in real time through a pre-trained Gaussian process regression agent model; and the decision-making unit performs three-dimensional visual rendering, jointly determines a high-confidence risk area based on the risk level and the uncertainty level, and generates an emergency prevention and control instruction with accurate spatial positioning. The problems that in the prior art, disaster evolution paths cannot be predicted in real time, and prevention and control measures are extensive are solved, millisecond-level online deduction and accurate active prevention and control of the thermal runaway process are achieved, and the safety of an energy storage system is remarkably improved.

The invention provides a petroleum and natural gas station operator labor protection appliance wearing detection method and system, and relates to the technical field of article detection, and the method comprises the steps: collecting the image data of an operator in real time through an image collection device, and calling a deep learning model to detect the wearing state of a labor protection appliance; according to the method, the technical parameters, the equipment state and the hazard source distribution data are combined to evaluate the hazard level and generate a wearable normative score and a risk trend value, a deep reinforcement learning algorithm is adopted to execute management and control measures, and detection parameters and processing strategies are continuously optimized, so that intelligent and dynamic management of safety protection of operators is realized, and the safety operation level of a station yard is improved.

The invention discloses a black soil erosion simulation system with rainfall runoff and snow melting confluence coupling effect. The system comprises a data acquisition system, a rainfall simulation system, a snow melting simulation system, a runoff collection and monitoring system, a black soil simulation area, a data processing and analysis system and a control system. The data acquisition module comprehensively collects various types of data of the black soil area; each simulation subsystem accurately simulates rainfall, snow melting and coupling processes; the runoff collecting and monitoring subsystem monitors runoff in real time; the black soil simulation area simulates the field condition; the data processing and analysis module deeply mines data, establishes a model and predicts a trend; and the control system realizes automatic accurate control. The whole system adopts a modular design and is convenient to maintain and upgrade. The operation, system flow, maintenance upgrading and application cases of each module are elaborated through a specific implementation mode, a powerful tool is provided for black soil erosion research, and a scientific basis is provided for making prevention and control measures.

The invention discloses a continuous rigid frame aqueduct cantilever casting hanging basket stress monitoring and early warning method and system, and relates to the technical field of hydraulic engineering construction.The method comprises the steps that stress monitoring points and displacement monitoring points are arranged, and a stress-displacement double-system collaborative sensing model is constructed; carrying out double-channel vibration noise separation on the stress signal and the displacement signal which are collected in real time; respectively calculating wet weight values based on a stress-wet weight nonlinear mapping relation and a displacement-wet weight geometric inversion model, and verifying the consistency of wet weight value measurement results through a residual checking mechanism; constructing a multi-source driving safety threshold model, and generating a dynamic safety threshold in real time; and when the monitored wet weight value exceeds the dynamic safety threshold value, triggering graded early warning and executing risk control measures. According to the stress-displacement dual-system independent measurement and cross checking method, through a vibration frequency domain compensation algorithm, vibration interference is suppressed, a multi-source driving safety threshold model is constructed, and high-precision monitoring of the stress of a continuous rigid frame aqueduct cantilever casting hanging basket is achieved.