CN-122024414-A - High-precision monitoring and linkage early warning device for stability of dredging and curing site cofferdam

CN122024414ACN 122024414 ACN122024414 ACN 122024414ACN-122024414-A

Abstract

The invention provides a high-precision monitoring and linkage early warning device for stability of a dredging and curing site cofferdam, which provides a set of comprehensive solutions of perception intelligence, accurate analysis, early warning, high management and control efficiency and collaborative optimization for ecological dredging engineering through an advanced hardware and software system integration and innovative core algorithm, and the early warning mechanism is in jump from post alarm to pre-judgment in advance, particularly cofferdam stability monitoring, scientific early warning based on structural mechanical state and safety coefficient is realized through introducing a finite element-limit balance coupling algorithm, the traditional alarm after measuring displacement exceeding standard is changed into early warning after calculating safety coefficient shortage, and quality change occurs to the scientificity, the foresight and the reliability of early warning, so that important safety accidents can be effectively prevented. The method effectively responds to various challenges in the background technology, represents the forward direction of the current intelligent water conservancy and intelligent construction site development, and has extremely high application value and popularization prospect.

Inventors

XIONG XIHUI
LIU HAIPENG
ZHONG SHILIN
ZHANG SHOUJUN
WU XIAOCHEN
LU WENTAO
GUAN YIBIAO

Assignees

上海海达通信有限公司

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (10)

1. High accuracy monitoring of desilting solidification place cofferdam stability and linkage early warning device, its characterized in that includes: the sensing layer comprises an environment monitoring terminal for collecting environment parameters, a safety and structure monitoring terminal for collecting safety and structure states, and a production and energy consumption monitoring terminal for collecting production and energy consumption data; The edge layer is in communication connection with the perception layer and comprises an edge computing gateway, and the edge computing gateway is used for converging and preprocessing data acquired by the perception layer; The central layer is in communication connection with the edge layer and comprises a server cluster for deploying a software system; The software system includes: The internet of things platform is used for accessing and managing the terminal equipment of the sensing layer; the data center is used for aggregating, cleaning and fusing the data from the internet of things platform; The intelligent analysis engine is used for performing intelligent analysis based on the fused data and generating early warning information, and at least comprises a cofferdam stability analysis model; The holographic digital twin platform is used for constructing and displaying a three-dimensional digital twin scene corresponding to the physical scene and dynamically driving the visualization of the early warning information; And the comprehensive business application portal is used for providing interactive interfaces for users with different roles.
2. The apparatus of claim 1, wherein the safety and structural monitoring terminal comprises a cofferdam stability monitoring sensor group comprising a fixed inclinometer for monitoring deep level displacement, an osmometer for monitoring pore water pressure, a buried strain gauge for monitoring structural strain, a radar level gauge or static level gauge for monitoring vertical settlement, and a surface displacement monitoring target for achieving surface displacement monitoring in conjunction with automated measurement equipment.
3. The apparatus of claim 1, wherein the cofferdam stability analysis model in the intelligent analysis engine is configured to perform the steps of: based on the monitored displacement boundary conditions and the pore water pressure data, the finite element equilibrium equation is solved Updating the stress field inside the cofferdam, wherein, The integral rigidity matrix of the cofferdam soil body is integrated by the rigidity matrix of each unit in the time step t, and depends on the soil body constitutive model and the current stress state; The node displacement increment vector of the time step t is stress field, which is the displacement variation of all nodes of the finite element grid from t-1 to t in the x, y and z directions; The equivalent node load increment vector of the time step t comprises the following parts of boundary condition load converted from newly monitored surface displacement through a mathematical method, load generated by effective stress change caused by newly monitored pore water pressure change, and limit balance method based on updated stress field, wherein the limit balance method is adopted, and the formula is as follows: calculating the integral safety coefficient of the cofferdam Wherein: n, the total number of vertical bars divided by the sliding soil body; Effective cohesion of the ith strip soil mass (geotechnical test parameters); the effective internal friction angle (geotechnical test parameter) of the ith strip soil body; arc length of sliding surface of ith strip; Pore water pressure at the center of the sliding surface of the ith bar (directly from osmometer monitoring values or finite element pore water pressure field interpolation); Total normal force acting on the ith bar sliding surface (by finite element stress field Performing integral calculation in the normal direction); Total tangential force (sliding force) acting on the ith bar sliding surface (by finite element stress field Integrating in tangential direction); Molecules Representing the sum of the anti-slip moments of all the bars on the sliding surface, wherein Is an effective normal force. The anti-slip force is calculated by the shear strength of the soil body And ) Providing; Representing the sum of the sliding moments of all the bars on the sliding surface, which are caused by the gravity of soil mass and external load.
4. The apparatus of claim 3, wherein the cofferdam stability analysis model is further configured to calculate an overall safety factor And comparing the risk level with a preset multilevel early warning threshold value, and generating structural early warning information containing the risk level and the treatment suggestion according to a comparison result.
5. The apparatus of claim 1, wherein the intelligent analysis engine further comprises: The environmental risk early warning model is used for judging pollution diffusion risks based on space-time characteristics of mud concentration and water quality data; the production work efficiency matching model is used for identifying productivity bottlenecks and giving scheduling suggestions based on real-time data of each production link; the AI visual identification service is used for analyzing the video stream to identify potential safety hazards; the energy consumption analysis and early warning model is used for analyzing the electricity consumption data and early warning abnormal energy consumption.
6. The apparatus of claim 1, wherein the holographic digital twin platform comprises: The three-dimensional geographic information engine is used for integrating the high-precision GIS map and the real three-dimensional model; the dynamic data driving module is used for binding and updating the real-time data of the Internet of things platform with the entity object in the three-dimensional scene; And the simulation and deduction module is used for supporting the dredging process simulation and the risk diffusion simulation.
7. The device of claim 1, wherein the sensing layer and the edge layer communicate data in a wired or wireless manner, and the edge layer and the center layer communicate encrypted data by using an industrial ring network or a wireless private network and adopting an MQTT or HTTPS protocol.
8. The apparatus of claim 1, wherein the data interaction control procedure of the apparatus comprises: the edge computing gateway receives and preprocesses the sensing layer data and then uploads the sensing layer data to the Internet of things platform; the internet of things platform triggers an abnormal event according to a preset rule and pushes the abnormal event to the intelligent analysis engine; the intelligent analysis engine calls a corresponding model to analyze and pushes the generated early warning information to the digital twin platform and the comprehensive business application portal; And the user performs early warning treatment and feeds back through the comprehensive service application portal to form closed-loop management.
9. The apparatus of claim 1, wherein the production efficiency matching model in the intelligent analysis engine utilizes a system dynamics simulation or operational study optimization algorithm to identify bottleneck links and generate scheduling advice based on dredging production, pipeline flow, sedimentation tank level, filter press duty cycle, earthwork inventory, and full chain data of vehicle and vessel locations.
10. A high-precision monitoring and linkage early warning method for stability of a dredging and curing site cofferdam is characterized by comprising the following steps: the method comprises the steps of S1, deploying a stereoscopic perception network, carrying out data fusion initialization, planning and arranging various sensors, establishing a mapping relation between the sensors and objects in a digital twin scene, and inputting initial parameters of a cofferdam to a stability analysis model; s2, constructing a holographic digital twin scene, fusing a live three-dimensional model, a GIS map and a key facility BIM model, and binding a real-time data stream with a twin scene entity; s3, performing multi-service flow parallel intelligent monitoring and primary early warning, performing real-time monitoring on environment, safety, production and energy consumption data, and triggering primary alarm based on a rule engine; s4, intelligent diagnosis and linkage early warning of cofferdam stability are carried out, when the change rate of the monitored data exceeds a threshold value, a stability analysis model is triggered, and the model executes the following operations: updating the cofferdam internal stress field based on the monitoring data; Calculating an overall safety factor based on an updated stress field ; According to The value generates hierarchical early warning information, and the early warning is issued through the digital twin platform, the application portal and the mobile terminal in a multi-channel linkage manner; And S5, carrying out collaborative scheduling and optimization based on full-chain data analysis, collecting and producing full-chain data in real time, identifying bottlenecks through an engineering matching model, giving out optimization suggestions, and realizing economic scheduling by combining energy consumption data.

Description

High-precision monitoring and linkage early warning device for stability of dredging and curing site cofferdam Technical Field The invention relates to the technical field of intelligent internet of things monitoring, in particular to a high-precision monitoring and linkage early warning device and method for the stability of a cofferdam of a dredging and curing site, electronic equipment and a computer readable storage medium. Background The dredging and solidifying engineering of wading and coasting is implemented in an ecologically sensitive area (such as a scenic spot), and the dredging and solidifying engineering is a system engineering with complex technology, high management difficulty and environment protection and safety risks. Traditional construction management modes face serious challenges in this class of projects, and are embodied in several aspects: 1. The environmental monitoring means is lagged, the environmental risk is high, the traditional environmental monitoring is mostly dependent on manual timing sampling and laboratory analysis, and continuous and real-time monitoring cannot be realized on key environmental indicators such as the mud concentration around the sewage isolation curtain, the residual water quality of the curing field and the like. There are risks of delayed discovery of pollution events and untimely emergency treatment, and ecological protection requirements of high-sensitivity areas such as scenic spots are difficult to meet. 2. The safety control relies on civil air defense, and the coverage has a plurality of risk points such as wide construction area, water-facing operation, deep foundation pit (cofferdam), large-scale mechanical operation and the like. The traditional safety management mainly relies on patrol and experience judgment of a safety officer, has the problems of visual field blind areas, fatigue and omission and the like, and cannot realize 7x 24-hour seamless perception and intelligent recognition of unsafe behaviors of personnel, abnormal states of equipment and instability symptoms of a cofferdam structure. 3. The engineering covers a plurality of links such as dredging, slurry conveying, filter pressing and dehydration, earthwork transportation, outward transportation and the like, and relates to various equipment such as ships, pump stations, filter presses, vehicles and the like. Under the traditional management mode, the information of each link is isolated, the dispatching depends on interphones and experiences, the full-chain production energy matching and dynamic optimization are difficult to realize, the bottleneck of upstream and other downstream or downstream blocking upstream is easy to occur, and resource idling and construction period delay are caused. 4. The cofferdam stability monitoring is extensive, and the early warning capability is insufficient, namely, the cofferdam of the curing field is used as a temporary water retaining structure, and the stability of the cofferdam is directly related to the field safety and the surrounding environment. Conventional monitoring typically employs total stations to manually measure displacement periodically, or to deploy a small number of simple sensors. The data acquisition frequency is low, the real-time performance is poor, the advanced early warning based on the structural mechanical state change cannot be realized due to the lack of the deep analysis and model evaluation of the monitoring data, and once instability occurs, the forward consequences are serious. 5. The energy consumption and cost management and control are fuzzy, and data support is lacking, so that project process equipment (such as a plate-and-frame filter press and a relay pump) is high in electricity consumption, and the traditional electricity charge management is only stopped at a total metering level, so that the energy consumption of each link and each equipment cannot be traced back finely. The problems of waste of energy and abnormal power consumption (such as line leakage and equipment idling) are difficult to find, and the cost control and the energy saving optimization are not facilitated. 6. The information is fragmented, decision command is not visual, various monitoring data, video pictures and scheduling instructions are distributed in reports of different systems and personnel, a project manager is difficult to quickly and comprehensively master global situation, and the command decision lacks uniform and visual platform support. In summary, the conventional management mode cannot meet the requirements of modern and high-standard ecological dredging engineering. An intelligent comprehensive management device (system) integrating global sensing, intelligent analysis, full-chain cooperation, three-dimensional visualization and linkage early warning is needed, engineering management is enabled by a scientific and technological means, and the method is environment-friendly, controllable, safe, reliable, excellent