303 results about "Control measure" patented technology

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 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 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 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 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 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 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 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 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.

The invention provides a subway tunnel water disaster risk evaluation method under extreme rainfall based on multi-source data, and belongs to the technical field of urban rail transit disaster prevention. The method comprises the following steps: establishing a grading evaluation system on the basis of field rainstorm monitoring data, hydrogeological parameters, field geological exploration, rock-soil physical and mechanical parameters, monitoring data of a tunnel lining structure and material strength parameters of a tunnel site area in combination with a plurality of developed tests, dividing corresponding grades and determining an influence index-; and S4, respectively carrying out weight calculation on the influence index-by adopting an analytic hierarchy process and an entropy weight method, then calculating a dynamic weight of the influence index-, correcting an abnormal dynamic weight, generating a comprehensive score, dividing risk grades, matching graded prevention and control measures, and optimizing and adjusting the prevention and control measures in real time through a parameter risk grade dynamic judgment and construction method matching mechanism. According to the method, minute-level accurate interpretation of a disaster evolution mechanism and dynamic optimization adaptation of a prevention and control scheme are realized.

The invention relates to an ultra-deep well perforation test combined operation tubular column safety evaluation method. The method comprises the following steps that S1, a packer lower portion perforation detonation annulus pressure evolution finite element model and a dynamic negative pressure evolution numerical model after perforation detonation are established; s2, establishing a perforation instant tubular column coupling impact vibration evolution model, and determining main control factors; s3, establishing a dynamic evolution numerical model of the shock absorber at the moment of perforation, and obtaining a dynamic evolution model of the lower oil pipe of the packer, the shock absorber and the perforating gun at the moment of perforation; and S4, evaluating the safety of the pipe column at the lower part of the packer by taking the Mises yield criterion as an evaluation standard. The influence of perforation detonation transient wellbore pressure and dynamic negative pressure on a lower tubular column system of the packer is fully considered, the dynamic response rule of the tubular column is disclosed through numerical simulation and theoretical analysis, risk control measures are put forward, and the safety of the packer is improved. Theoretical basis and technical support are provided for optimal design of an ultra-deep well perforation test combined operation scheme and safety of an oil testing process.

The invention provides a bridge health monitoring and intelligent control method and system based on machine learning, and the method comprises the steps: carrying out the distributed sensing data collection of a bridge structure, generating a structure monitoring data set, carrying out the spatial-temporal feature extraction, and obtaining a vibration response feature sequence and a traffic load feature sequence of the bridge structure; performing correlation analysis on the two sequences, generating a structural damage sensitive feature matrix, performing damage evolution trend prediction based on the structural damage sensitive feature matrix, generating damage development probability distribution of the bridge key component, and generating a dynamic traffic control instruction according to the damage development probability distribution. The dynamic traffic control instruction comprises a vehicle passing parameter dynamically adjusted based on a damage degree quantitative index, and sending the dynamic traffic control instruction to a traffic management system. According to the invention, the accuracy of bridge health monitoring and the dynamic adaptability of control measures can be improved.

The invention discloses an early warning grade evaluation method based on an urban pollution industrial site, relates to the technical field of industrial site early warning evaluation, and solves the technical problems of incomplete overall evaluation and lack of systematic analysis process and data processing correction caused by analysis of a single evaluation index. According to the method, pollution factor indexes are integrated in a three-dimensional mode through the pollutant standard exceeding multiple mean value, the specific pollutant toxicity coefficient and the pollution plume diffusion area proportion, the pollution intensity is reflected, toxicity hazards and spatial diffusion risks are considered, the soil pollution area is calculated through the Kriging interpolation method, the groundwater pollution plume area is predicted through the MODFLOW model, and the groundwater pollution plume area is predicted through the MODFLOW model. The overlapping area is processed through a maximum area method, repeated statistics is avoided, the accuracy of pollution range data is improved, different threshold values are set, standardized matching of comprehensive indexes and early warning levels is achieved, corresponding management and control measures are defined for all the early warning levels, abstract comprehensive indexes are converted into specific practical operation guidance, and hierarchical management and control can be conveniently carried out.

The invention provides a train forward operation risk assessment method based on an operation risk domain, and the method proposes the concept of the operation risk domain based on a field theory, constructs a train forward operation risk assessment model based on the risk domain, and constructs operation risk domain models in two scenes of train tracking and foreign matter invasion. And the forward operation risk of the train is evaluated in real time, and management and control measures are given, so that the real-time and quantitative evaluation of the forward operation of the train is realized, and the construction of an autonomous safety guarantee system of rail transit is promoted.

This invention discloses a state monitoring method for distribution cable branch boxes based on state recognition, specifically relating to the field of power monitoring technology. It involves collecting multi-dimensional operational data from multiple branch boxes over a continuous time period to construct a state evolution sequence; calculating state deviation scores and adjacent equipment state consistency scores to determine whether an assessment process is triggered; after triggering, constructing a state influence probability map, identifying abnormal influence paths, extracting state disturbance diffusion indices and cooperative behavior deviation indices, inputting these into a pre-trained risk identification model, generating risk level intervals and causal probability distribution vectors, executing influence control measures, and updating the state recognition logic. This invention achieves dynamic perception of branch box states by constructing a state evolution sequence, combines state deviation scores and adjacent equipment state consistency scores to achieve joint judgment of individual and group behaviors, and drives control strategies and map updates using risk level intervals and causal probability distribution vectors, thereby improving monitoring accuracy and system adaptability.

The invention discloses a cage rearing broiler chicken multi-level epidemic situation early warning and prevention and control optimization method, which comprises the following steps: S1, constructing a data support system; S2, constructing a cross-level epidemic situation propagation simulation system, and realizing multi-scale digital mapping from a coop to a region; s3, according to the epidemic situation simulation result in the S2, early warning grades are divided; and S4, according to the early warning level in the S3, an association rule base addition and deletion measure is called, a candidate prevention and control measure combination is generated, and dual-target optimal measure combination screening with the maximum control effect and the minimum total cost is realized. According to the method, multi-scale accurate simulation of epidemic situation propagation and quantitative optimization of prevention and control measures can be realized, the control effect and cost are balanced, the scientificity and operability of prevention and control decisions are improved, and the method is adaptive to different early warning levels and breeding scenes and is high in robustness and practicability.

The invention discloses a hydroelectric generating set data acquisition and analysis method, system, equipment and medium, and belongs to the technical field of data acquisition and analysis, and the method comprises the steps: obtaining various original data of the operation of a hydroelectric generating set, and achieving the information collection of the set and the operation environment; performing multi-level structured modeling and node feature extraction on the acquired data by adopting a mode of combining a knowledge graph and a graph convolutional neural network, and analyzing an operation state and a future trend of a unit; according to a data analysis result, comparing a historical working condition with a scheduling task, judging whether the operation of the unit is in a reasonable interval, and triggering a corresponding regulation and control measure when the operation is abnormal; by monitoring the state and operation data of key equipment in real time, actual and reference parameters are compared, potential fault risks are identified, early warning information is generated, and data archiving and statistical analysis are carried out; and carrying out visual display on data acquisition, analysis, scheduling and early warning results. Collected data are effectively utilized, and scheduling decision and early warning maintenance are powerfully supported.

The invention relates to a line adjustment and electronic adjustment combined security card control system, method, device and equipment, a storage medium and a program product. The system comprises a state information acquisition module which is used for acquiring train number window information from a dispatching centralized control system under the condition of receiving a power failure and transmission operation application initiated by a power supply dispatching management system, acquiring planned power supply operation information from a power supply dispatching management system and acquiring power supply arm coverage information from a data acquisition and monitoring control system; the intelligent association analysis module is used for inputting the train number window information, the planned power supply operation information and the power supply arm coverage information into a pre-trained risk assessment model, analyzing the risk association degree of the train operation trend and the power supply operation through the risk assessment model, and outputting a risk assessment result according to the risk association degree; and the safety card control execution module is used for executing safety card control measures corresponding to the risk assessment result. By adopting the system, the running safety of the train can be improved.

The invention discloses an operation machine safe operation control method and device based on a three-dimensional electronic fence, and belongs to the technical field of operation machine control. The method comprises the steps of setting a three-dimensional electronic fence based on an actual working environment of a working area; acquiring a first dynamic parameter of the working machine, wherein the first dynamic parameter comprises first three-dimensional position data and first attitude data when the working machine works; calculating a key point coordinate on the operation machine according to the first dynamic parameter; calculating the minimum boundary distance between the key point and the three-dimensional electronic fence according to the coordinates of the key point; and determining and executing a safety control measure according to the minimum boundary distance. According to the method, the relative position relation between the key point of the operation machine and the three-dimensional electronic fence can be accurately determined when the position of the operation machine changes, manual recalibration and confirmation are not needed, dynamic position self-adaption of the operation machine is achieved, misoperation or collision accidents are avoided, and construction safety is remarkably improved.

The invention discloses an intelligent monitoring platform and method for construction flying dust, and relates to the technical field of flying dust monitoring data processing, and the platform comprises the steps: traversing a building construction region, laying a plurality of monitoring devices for data collection, and obtaining a plurality of construction monitoring data; determining a flying dust data association network; performing flying dust prediction on the plurality of construction monitoring data to obtain a flying dust prediction result; traversing the building construction area according to the flying dust prediction result for tracing; and optimizing the flying dust treatment scheme, and generating a flying dust treatment optimization scheme to intelligently treat flying dust in the building construction area. The technical problems of low dust pollution monitoring efficiency and insufficient treatment scheme pertinence caused by difficulty in real-time and comprehensive monitoring of a building construction area and incapability of accurately positioning a dust pollution source in the existing building construction dust monitoring treatment are solved, intelligent management of building construction dust treatment is realized, and the construction efficiency is improved. The technical effect of improving the dust pollution monitoring efficiency and the treatment pertinence of the building construction area is achieved.

The invention discloses a flexible blocking solid source early warning structure suitable for narrow and steep debris flow prevention and control, and relates to the technical field of debris flow prevention and control. The structure is selectively arranged in a circulation area formed by debris flow, is reasonably combined and arranged from upstream to downstream according to the channel length and the longitudinal slope, and at least comprises four stages of flexible carbon fiber grating net structures and a counterfort type rubber dam. According to the flexible blocking and source fixing early warning structure suitable for narrow and steep debris flow prevention and control, the blocking and source fixing effects are achieved, early warning can be conducted in advance, the structure can bear high-strength impact of narrow and steep debris flow, impact loads are dispersed, damage of debris flow impact force to the blocking structure is greatly reduced, and the flexible blocking and source fixing early warning structure is light, green and convenient to use. According to the transportation and installation scheme, the construction period is short, the difficulty is low, debris flow disasters are accurately predicted, the problem that a steep debris flow prevention and control structure is prone to failure is solved, and the investment benefits of prevention and control measures are exerted to the maximum extent.

The invention discloses an argillaceous siltstone geological anchorage deep foundation pit construction method, which relates to the technical field of building construction, and comprises the following steps: collecting stratum structure data, hydrogeological data and surrounding environment data of an anchorage area; determining an integral foundation pit form; a supporting scheme is designed, and a supporting structure type, a construction process and safety prevention and control measures are determined; verifying the stability of the supporting structure; construction resource investment, construction period and risk assessment data of different support schemes are collected, cost-benefit comparative analysis is carried out, and an optimal construction scheme is screened; the construction state is fed back in real time; and the anti-collapse, anti-seepage and anti-disturbance construction requirements under the muddy siltstone geological scene are met. According to the deep foundation pit construction method, a deep foundation pit construction full-process solution matched with the geological scene is formed by adopting double-row secant pile supporting, a targeted cast-in-situ bored pile and anchor cable construction technology and a near-high-rise special monitoring and inner supporting optimization technology.

The invention provides an enterprise safety production risk assessment system, which comprises a task library module used for acquiring and screening internal and external operation tasks of an enterprise area. And the step module is used for carrying out step decomposition on each operation task according to an execution function. And the hazard module is used for identifying hazard information in the enterprise operation process. And the risk analysis module is used for identifying the risk possibly caused by the hazard and analyzing the process sequence of converting the risk into the risk and causing the accident. And the risk assessment and grading module is used for carrying out element valuing, calculation and grading processing on the risk. And the control measure module is used for determining a risk response mode and a control measure. And the action plan module is used for generating an action plan and prompting the implementation condition of the risk control measures to the user. According to the system, the whole-process informatization operation of operation risk assessment is realized, the relevance between an accident sequence and a protective measure is particularly deeply analyzed, and the accuracy of risk assessment and the pertinence of a control measure are improved.

The invention relates to the technical field of engineering safety monitoring and intelligent construction management, in particular to a slope deformation automatic identification and risk early warning system and method in the earthwork construction process, and the method comprises the following steps: collecting slope image data and sensor monitoring data, and carrying out the time synchronization processing of the data; extracting deformation characteristics of the slope surface, and generating a risk assessment index threshold in combination with historical data; performing feature analysis on the synchronized monitoring data on a short-term and long-term dual-time scale, and extracting a deformation rate and an accumulated trend index; setting a scene gating factor; calculating a comprehensive risk score; eliminating short-time interference influence; and judging the risk level, and executing early warning response of the corresponding level in a linkage manner, including sound-light alarm and engineering control measures. According to the method, the problems of space-time non-uniformity and insufficient continuity of the sensing layer, insufficient evolution description of the cognitive layer and weak closed loop and edge-cloud collaboration of the execution layer are solved.

The invention provides a rock burst prevention and control measure evaluation method and device and a medium, and the method comprises the steps: obtaining prevention and control measure data which comprises a prevention and control measure implementation engineering drawing and various monitoring data; in the simulation software, a three-dimensional model conforming to an actual mine is established, geological parameters and control measure data of underground rock mass of the mine are input, dynamic numerical analysis is conducted through the simulation software, numerical simulation results are obtained, and the numerical simulation results at least comprise a stress concentration coefficient and the vertical distance between a stress concentration area and a roadway; acquiring micro-seismic monitoring data within a preset time period after implementation of the prevention and control measures; and judging the effect of the rock burst prevention and control measure according to the stress concentration coefficient, the vertical distance between the stress concentration area and the roadway and the micro-seismic monitoring data. The method solves the problem of limitation of evaluating rock burst prevention and control measures by a single index in the prior art.

The invention discloses a mountainous area highway traffic risk short-time prediction method and system, relates to the technical field of intelligent traffic, and solves the problems that a traditional traffic risk prediction method is single in data dimension and inaccurate in scene division. According to the technical scheme, the method is characterized in that a multi-source heterogeneous data set including historical accident data, road linear data, real-time traffic flow data and meteorological data is constructed; performing clustering analysis on the multi-source heterogeneous data set by adopting a clustering algorithm, determining core clustering features, and dynamically correcting a membership function in combination with road linear data to divide scenes to obtain classified scenes; determining a risk level of the classification scene through a pre-constructed gradient boosting tree model and a long short-term memory network hybrid model; and triggering a grading early warning signal according to the risk grade, and issuing early warning information and a dynamic management and control measure corresponding to the risk grade based on the grading early warning signal. The effects of improving the risk analysis data coverage rate, being more accurate in scene division and improving the risk prediction precision are achieved.

The invention relates to the technical field of coal mine safety mining and rock stratum control, and discloses a deep roadway surrounding rock multi-parameter dynamic regulation and control method which comprises the following steps: firstly, dividing a monitoring area and collecting gas pressure, surrounding rock displacement, gas release amount and stress data in real time; then calculating a gas pressure influence index and a surrounding rock stress response coefficient representing multi-field coupling characteristics based on the collected data; inputting the characteristic indexes into a pre-trained multiple regression model, and calculating to generate a surrounding rock stability evaluation coefficient of each region; and finally, dynamically dividing the monitoring area into a stable area, an early warning area or an unstable area according to a comparison result of the evaluation coefficient and a set threshold interval, and respectively executing differentiated support regulation and control measures such as current situation maintenance, support strength increase or grouting reinforcement. According to the method, dynamic accurate evaluation and active fine management of the stability of the surrounding rock of the deep roadway are realized by quantifying the coupling effect of a gas field, a stress field and a displacement field.

The invention discloses a flood season water environment quality monitoring system based on wireless communication, and relates to the technical field of water environment monitoring, the system comprises the following components: a data acquisition and transmission module, an intelligent early warning module, a traceability analysis module and a decision support module; the flood season water environment quality monitoring system integrates a data acquisition and transmission module, an intelligent early warning module, a traceability analysis module and a decision support module, realizes comprehensive and real-time monitoring of flood season water environment quality, and can rapidly capture water quality changes and timely send early warning information by means of a high-precision (high-resolution) water quality monitoring sensor and an advanced wireless communication technology, thereby realizing the purpose of flood season water environment quality monitoring. And the pollution source is accurately traced, and scientific treatment decisions are provided for related departments, so that the water environment quality monitoring efficiency and accuracy are greatly improved, effective prevention and control measures can be taken in time, water quality deterioration is prevented, and the safety of water resources is protected.

The invention discloses an intelligent construction site vulnerability identification and prevention and control method based on sensing terminal data, and particularly relates to the technical field of safety risk management and control. The method comprises the following steps of: acquiring and preprocessing multi-source sensing terminal data to construct standardized monitoring data; constructing a unified space-time association layer based on the standardized monitoring data, identifying a function dependency relationship and a space association mode between different construction processes, and determining a key resource occupation relationship and a space interaction mode; identifying dynamic cascade effect characteristics caused by construction rhythm fluctuation between construction procedures; and finally, fusing the space and time sequence coupling strength, determining a key vulnerability conduction path, and formulating a corresponding grading early warning strategy and active prevention and control measures based on the risk grade of the conduction path, thereby realizing accurate identification and active management and control of the vulnerability risk of the intelligent construction site. The prevention and control efficiency and the active response capability of the construction site risk are improved, and safe and stable operation of the intelligent construction site is guaranteed.