1877 results about "Structural safety" patented technology

The invention discloses a wind turbine generator hoisting construction tower drum operation system and a construction method thereof, and relates to the technical field of intelligent control. The problems that in the prior art, a static tension balance mechanism cannot restrain bending moment abrupt change, steel-concrete interface stress concentration causes microcrack propagation, the wave dynamic load compensation capacity is insufficient, and dynamic rigidity attenuation early warning is lacked are solved. Comprising a dynamic load prediction module, a multi-mode vibration suppression module, an offset compensation module and a digital twinborn decision module, a hoisting load is solved in real time through a multi-physics field coupling model and an improved time sequence deep learning algorithm, and interface crack propagation is suppressed in combination with traveling wave offset control and sweep frequency vibration. An improved Morison equation is adopted to drive a two-stage hydraulic servo to compensate a wave dynamic load, and a digital twin closed-loop correction mechanism is constructed based on a 5G URLLC protocol; the tower drum hoisting precision, the structural safety and the operation reliability under the complex working condition are remarkably improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention discloses a system and a method for predicting the fatigue life of a flange by utilizing ultrasonic flaw detection, and belongs to the field of artificial intelligence. Comprising an aggressive frequency domain spot feature extraction module, a microdefect enhancement recognition module, an acoustic echo database training module, a damage accumulation-stress wave mapping model construction module, an active echo disturbance injection module, a fatigue life progressive prediction module, a real-time feedback closed-loop control module and a multi-scale self-checking redundancy mechanism module. Spot feature extraction and defect identification share high-frequency phase data to generate a focusing map, and registration with historical defect features is carried out in a database; the mapping model constructs a stress-damage relation according to the stress concentration coefficient and the frequency domain characteristics; the disturbance injection module applies directional excitation to potential fatigue points, and the prediction module constructs a life evolution curve based on response; the feedback control module fits and corrects the database and the disturbance strategy; the self-checking mechanism improves the prediction robustness and self-adaptability through waveform deviation and mismatch correction. The beneficial effects are that the structure safety and the service life management intelligence are improved.

The invention relates to the technical field of crack detection, in particular to a building crack intelligent detection and prediction method based on multi-modal data fusion, which comprises the following steps: constructing a multi-source heterogeneous data set including an image data set, a thermal imaging data set and a vibration data set; simulating complex environment interference through a GAN (Generative Adversarial Network), and dynamically enhancing image training data; designing a double-branch multi-mode fusion network, and extracting the edge and texture features of the crack; designing a multi-modal fusion branch, and carrying out feature alignment and weighted fusion on vision, thermal imaging and vibration data; and based on a long short-term memory network LSTM and a structural mechanics model, a crack trend prediction module is developed, and early warning is provided for building structure safety. According to the method, intelligent monitoring and early warning of building structure safety are realized by constructing a multi-source heterogeneous data set, enhancing image training data through a GAN, extracting features by using a double-branch multi-mode fusion network, predicting a crack trend based on an LSTM and a structural mechanical model, and finally integrating system deployment.

The invention relates to the technical field of building curtain wall keel structure design, and discloses a curtain wall keel structure intelligent optimization design method, which comprises the following steps: firstly, obtaining geometric parameters, load distribution data and material performance indexes, and verifying the geometric parameters, the load distribution data and the material performance indexes; analyzing the verified data to generate multi-dimensional structure characteristic parameters, and constructing a structure optimization reference model; keel structure data are verified in real time according to the model, and abnormal matching of node displacement and stress distribution is quantitatively evaluated; according to a verification result, generating a structural anomaly index to judge a design defect; and when defects exist, optimization design schemes including keel section adjustment, node reinforcement and the like are automatically generated and sequenced. According to the method, multiple factors are comprehensively considered, multi-objective optimization is achieved, the curtain wall keel structure design quality and efficiency can be improved, the structure safety is guaranteed, and the cost is reduced.

The invention relates to the technical field of structural health monitoring and geotechnical engineering, and discloses a breakwater foundation settlement direction real-time monitoring and early warning system and method, and the system comprises a data collection module which is used for obtaining the measurement data of at least one sensor disposed on a breakwater and a foundation of the breakwater in real time; the storage module is used for storing a preset physics-based reduced-order model, and the reduced-order model represents the overall deformation rule of the breakwater and the foundation system through a group of core deformation modes; and the processing module is connected with the data acquisition module and the storage module, and the processing module comprises a data assimilation module which is used for fusing the measurement data and the order reduction model in real time. By arranging the deformation field reconstruction module, the problem that only discrete point information can be obtained through traditional monitoring is effectively solved, real-time and high-precision expansion from sparse measurement to a continuous overall deformation field is achieved, and a more visual and reliable basis is provided for comprehensively evaluating the safety state of a structure.

The invention discloses a BIM-based hoisting construction supervision optimization management system. The system comprises a sensing layer which collects multi-source heterogeneous data in real time; according to the decision-making layer, a bottom layer utilizes an incremental RRT # algorithm to generate candidate paths meeting crane kinematics constraints, a distributed Q-learning framework is embedded in an upper layer, all cranes serve as independent agents, collaborative learning is carried out through a shared experience pool, a path planning strategy is dynamically optimized according to environment sensing data and construction progress requirements, and a path planning strategy is established. Meanwhile, a fuzzy sliding mode control algorithm is developed to be combined with an LSTM-Transformer crane cart and trolley walking speed, a hook crane cart and trolley walking speed, a hook lifting speed and a steel wire rope disturbance prediction model to calculate crane motion compensation parameters, and an optimal control instruction is generated in advance based on predicted crane moving walking and lifting data; the execution layer is used for issuing the instruction generated by the decision-making layer to construction equipment; and the optimization layer is used for constructing a BIM model and feeding back an equipment execution result and structure safety monitoring data to the decision-making layer.

The invention discloses a multi-parameter collaborative optimization design method for lateral impact resistance of a honeycomb sandwich panel battery pack, and relates to the technical field of structural safety and lightweight design of new energy automobile battery packs. An existing battery pack generally has the defects of insufficient light weight, limited energy absorption efficiency and lack of multi-parameter collaborative optimization with an upper box body, a lower box body and a stiffening beam in impact resistance research. Therefore, according to the honeycomb sandwich panel battery pack lateral impact resistance multi-parameter collaborative optimization design and parameter calculation method, a honeycomb sandwich panel finite element model is established, performance parameters are obtained, an optimal energy absorption combination is screened, then a battery pack box body finite element model is established, and multiple design variables are set; and constructing a function relationship between variables and responses by utilizing an agent model, and carrying out multi-objective optimization by combining an NSGA-II algorithm to finally obtain an optimal design parameter meeting a safety threshold and carrying out simulation verification. The method is suitable for battery pack anti-impact protection design work of the electric automobile under the lateral collision working condition.

The invention provides an automatic evolution method and system for a building design parameterization data driven scheme, and relates to the field of building design, and the method comprises the steps: firstly obtaining a building design parameterization basic data set containing building function requirements, structural safety, spatial layout, environment adaptation and other parameters; the method comprises the following steps: generating an architectural design parameter dynamic constraint model by using a parameter constraint dynamic iterator, generating an initial scheme through a parameterization scheme intelligent evolution model, starting a scheme iteration evolution closed-loop process to obtain an intermediate scheme, and finally screening out a target architectural design evolution scheme and outputting related parameterization process associated data. Automatic and intelligent generation and optimization of the building design scheme are realized.

The invention discloses a digital modeling steel structure multi-dimensional collaborative optimization design method and system, belongs to the technical field of steel structure design, and aims to solve the problems that multi-dimensional comprehensive consideration and automatic optimization tools are lacked, and complex multi-objective optimization requirements are difficult to quickly respond. Comprising the steps of establishing a full-parameterized digital model containing construction process parameters and environmental protection indexes, constructing a multi-objective evaluation system of structural safety, economical efficiency, construction feasibility and environmental influence, performing automatic optimization by adopting an improved multi-objective evolutionary algorithm, selecting 10 groups of candidate schemes, generating a visual interface, supporting human-computer interaction decision, and generating an optimization report. According to the method, multi-dimensional collaborative optimization is realized by constructing a multi-target evaluation system of structural safety, economy, construction feasibility and environmental influence, construction process parameters and environmental protection indexes are integrated through the digital modeling unit, it is ensured that factors of all parties are comprehensively considered in design decision, and the design efficiency is remarkably improved.

The invention discloses a dynamic response regulation and control and vibration reduction system and method for a floating type photovoltaic platform in the technical field of offshore new energy equipment, and aims to improve the anti-wave stability and structural safety of the platform under complex sea conditions. The system comprises a platform main body, a monitoring unit, a response analysis and early warning unit and a regulation and control execution unit, the monitoring unit is arranged on the platform main body and is used for collecting real-time sea condition data; the response analysis and early warning unit is in communication connection with the monitoring unit and is used for performing response analysis on the real-time sea condition data and sending an early warning signal to the regulation and control execution unit when the abnormal working condition of the platform main body is identified; and the regulation and control execution unit is used for carrying out regulation and control and vibration reduction on the platform main body according to the received early warning signal. According to the method, the assembly fatigue can be obviously relieved, the cable damage probability is reduced, the service life of the platform is prolonged, and good engineering adaptability and intelligent expansion potential are achieved.

The invention provides an arch dam shape efficient intelligent optimization method and system based on an agent model. The method comprises the steps that an arch dam physical-numerical model is constructed, a double optimization target with structural safety and economical efficiency as the core is determined, design parameters are selected, a constraint function is set, and an evaluation index system is established; samples are generated through Latin hypercube sampling, a data set is constructed in combination with finite element calculation, and a multi-task learning architecture is adopted to train a high-precision agent model. And then, coupling the proxy model with a multi-objective optimization algorithm, quickly searching a Pareto optimal solution set, and screening out a comprehensive optimal figure by using a multi-attribute decision-making method. And finally, through a finite element simulation verification result, prediction precision and performance improvement are ensured. The method has the advantages of lightweight modeling, efficient prediction and accurate search, and provides an effective tool for intelligent optimization and rapid decision making of hydraulic structures such as arch dams and the like.

The invention provides a deep sea temporary platform large-diameter steel casing dynamic response analysis method, and relates to the technical field of deep sea engineering, and the method comprises the steps: building a three-dimensional explicit dynamic coupling model according to the typhoon frequent generation characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, the wave load is decomposed, the vibration hammer load effect, the ocean current load effect and the internal wave current load effect are integrated, and a comprehensive dynamic load set of the steel casing is determined. By establishing a precise model, integrating multi-source loads, identifying a risk area, monitoring stress in real time and optimizing design parameters based on data, self-adaptive matching of steel casing design and a complex marine environment is achieved, the structural safety of the deep sea temporary platform steel casing is effectively improved, and the cost is reduced.

The invention relates to the technical field of bumper detection, in particular to an automobile bumper detection method and system, and the system comprises a rigid test bench, a multi-point adjustable impact loading module, a bumper surface deformation monitoring module, and a control and processing module. The to-be-tested automobile bumper is fixed on the test bench according to a preset mode, and the plurality of sensors are arranged based on a preset position; carrying out dynamic and static balance test on the to-be-tested automobile bumper according to the received control instruction, and carrying out corresponding index calculation; on the basis of static and dynamic test indexes, a quantitative report is generated, whether the balance of the bumper structure reaches the standard or not is automatically judged, complex static supporting and dynamic impact working conditions possibly encountered by the bumper on a vehicle can be effectively simulated in an integrated process, the static stability of the overall structure and the coordination under power impact of the bumper can be quantitatively evaluated, and the reliability of the bumper structure is improved. Therefore, the structural safety performance of the bumper can be reflected more truly.

The invention discloses a multivariable self-adaptive collaborative optimization control system and method for a high-alkali coal boiler, and relates to the technical field of high-alkali coal boiler optimization control, the system is based on real-time operation data acquisition and multi-working-condition state recognition, and dynamic evaluation and collaborative adjustment of combustion heat transfer and physical coupling effects are achieved; by collecting a first data set, a second data set and a third data set, a high-alkali coal quality and load mapping relation is constructed, and a multi-working-condition state label is output; based on the label, the multivariable cooperative adjustment module calculates a combustion heat transfer coupling coefficient and performs efficiency judgment; the physical coupling effect evaluation module evaluates the boiler internal structure risk; and the dynamic control optimization module integrates multiple indexes to realize control response optimization. According to the system, through threshold value judgment and strategy feedback, the combustion efficiency of the high-alkali coal boiler is improved, structural safety guarantee and dynamic regulation and control optimization are achieved, and the operation stability and economical efficiency of the high-alkali coal boiler are remarkably improved.

The invention provides a container shipping stowage method and system based on a multi-stage optimization algorithm, is applied to the technical field of operational research and port logistics, and decomposes a complex stowage process into a plurality of optimization stages based on a four-stage mixed integer programming method. The method comprises the following steps: firstly splicing 20-foot boxes into 40-foot equivalent boxes, quickly locking a shipping space by taking the minimum box area-ship-shellfish connection number and weight deviation as targets, then eliminating vertical overweight and transverse unbalance layer by layer, and finally finely adjusting the shipping space through local neighborhood search, thereby reducing the box turnover rate and improving the dispatching efficiency. Therefore, higher efficiency, lower conflict rate and better process matching degree of container space distribution are realized, the adaptability and efficiency of actual wharf operation are remarkably improved while the safety of a ship structure is guaranteed, and intelligent, systematized and efficient development of a port operation system is promoted.

The invention discloses a concrete dam intelligent online monitoring method based on multi-source data fusion. The method comprises the following steps: S1, acquiring monitoring data; s2, judging the validity of the monitoring data; s3, monitoring data abnormity identification; s4, performing on-site inspection information abnormity identification; s5, structure safety rechecking is carried out; s6, comprehensively evaluating the safety condition of the dam; according to the invention, a full-service process closed-loop management system is constructed, so that full-link intellectualization from data acquisition to security decision is realized.

The invention relates to the technical field of parameter optimization, and discloses a steel-concrete composite beam bridge prefabricated segment prestress tension parameter optimization method and system, and the method comprises the steps: obtaining a design BIM model, classifying prefabricated segments, constructing an initial digital twinborn model, and obtaining monitoring data; processing the monitoring data to obtain corrected data; comparing the corrected data with a model simulation result, and calibrating the model to generate a precise digital twinborn model; evaluating a multi-dimensional robustness index to construct a feature vector; generating a self-adaptive tensioning strategy sequence through a graph neural network in combination with the feature vector and the precise model; according to the method, intelligent optimization of the tensioning parameters is achieved, and the construction precision and the structural safety of the steel-concrete composite beam bridge are remarkably improved.

The invention discloses a high-pier large-span extra-large bridge suspended pouring beam hanging basket construction method, and belongs to the field of bridge engineering construction.The method comprises the following steps that hanging basket rails are installed, the precision is controlled, and a hanging basket comprising a double-channel-steel main truss, a manganese steel sling, an arc-shaped flow guide plate and a honeycomb formwork is assembled; grading pre-pressing is carried out according to the specified multiple of the design load, and the pre-camber of the template is corrected by adopting a BP neural network to ensure that the finished bridge linear error meets the requirement; a mechanical-hydraulic-electrical integrated intelligent control system is deployed, and the traveling speed and synchronism of the hanging basket are controlled through double-oil-cylinder driving, a 5G digital twin platform and an edge computing technology, so that rapid alarm is realized when the load is abnormal; segmental circulation construction is carried out, and when the environment wind speed reaches a set value, a hydraulic rail clamping device is automatically locked; a model is established by using ANSYS software, and simulation verification is performed on multiple types of key working conditions to ensure that the safety coefficient of the structure meets the specification requirement. The construction precision and safety are guaranteed, and the method adapts to complex environments.

The utility model relates to a flexible photovoltaic support with a vibration reducing function, which comprises end part stand columns and a middle stand column, the middle stand column is arranged between the end part stand columns, the top of each end part stand column is fixedly connected with an installation cable, and two installation cables are arranged in pairs in parallel in a group. A triangular support is fixedly connected to the mounting cables arranged in pairs, a spring is fixedly connected to the bottom of the triangular support, a limiting piece used for limiting straightening of the spring is installed on the spring, a balancing weight is fixedly connected to the bottom of the spring through a first steel wire rope, and a ground pile is fixedly connected to the bottom of the balancing weight through a second steel wire rope. Various guarantees are provided, the vibration amplitude of the flexible photovoltaic support is effectively reduced, the structural safety is improved, meanwhile, the vibration reduction assembly can be effectively protected, and structural failure is avoided.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.

The invention relates to the field of bridge construction, and discloses a hexagonal-section large-cut-angle and dip-angle combined tower steel-concrete combined section construction method which comprises the following steps: arranging a strain sensor group and a laser calibration target spot on the outer wall of a steel shell, establishing a pose parameter mapping model through multi-source data fusion, and resolving the six-degree-of-freedom pose of the steel shell in real time; constructing a load matrix based on the strain data, and solving a target stress value of the support rod by adopting a maximum flow-minimum cut algorithm; by adjusting the telescopic amount of the telescopic supporting rod in stages and combining servo motor driving and a closed-loop feedback correction mechanism, balanced distribution of supporting force is achieved; the weight coefficient of the multi-objective optimization algorithm is dynamically adjusted, and the pose precision and the structure safety requirement are balanced; and finally, after the pose convergence and stress balance conditions are met, the driving device is dismantled, and the supporting rod body is reserved to serve as a permanent reinforcing structure. The technical problem that in a traditional method, the steel shell pose precision and the stress balance of a supporting system are difficult to consider at the same time is solved.

The invention belongs to the technical field of bridge manufacturing, and discloses a highway bridge manufacturing method and system based on deep learning, and the method comprises the steps: data collection and preprocessing: collecting bridge design parameters, construction state data and environment feature data through a BIM model, a sensor network and construction logs, and constructing a multi-modal input data set; performing cleaning, normalization and feature coding on the data to generate a structured data matrix; multi-task deep learning model construction: constructing an integrated deep neural network; joint training and optimization: adopting a multi-objective loss function, fusing design compliance, construction efficiency and structural safety indexes, and realizing global optimization of model parameters through a gradient descent method; an adversarial generative network is introduced to perform data enhancement on historical fault samples, and the generalization ability of the model is improved. According to the invention, the deep learning technology is comprehensively applied to the whole process of bridge manufacturing, and intelligentization of design, construction, control and knowledge management is realized.

The invention provides a structural design optimization method and device for an underground station constructed through a shield tunneling method, and belongs to the technical field of underground engineering.The method comprises the steps that site geological survey data of a station trepanning area are obtained and input into a three-dimensional finite element model, and an initial three-dimensional model is constructed; analyzing a dynamic load effect in a construction stage by combining a load reduction theory to obtain a target three-dimensional model; finite element analysis is carried out based on the target three-dimensional model, and the maximum principal stress of the edge of the opening is extracted; then comparing the maximum principal stress with the allowable stress of the material, and performing load reduction iteration under the condition that the maximum principal stress is greater than the allowable stress of the material to obtain a final load reduction coefficient; and taking the final load reduction coefficient, the trepanning center coordinate and the stress concentration factor as the input of a two-dimensional reinforcement model, generating an equivalent live load, calculating the reinforcement amount, and automatically realizing the reinforcement optimization design. The construction efficiency is improved and the engineering cost is reduced while the safety and stability of the structure are ensured.

The invention discloses a concrete structure tall formwork supporting system stress deformation actual measurement method, and relates to the technical field of constructional engineering structure safety, the method comprises the following steps: determining initial stress distribution reference data through a pre-established supporting system geometric model database, and determining final construction adjustment guidance data; according to the final digital construction scheme, a construction safety assessment report is automatically generated, final conclusion data of safety assessment is determined, a result is fed back to the design optimization module through the data transmission module, and the unsolved uneven stress or deformation problem is extracted from the feedback data; obtaining input parameters of a new round of design optimization, and obtaining a continuously improved design iteration result; according to the actual measurement method for stress deformation of the high and large formwork supporting system of the concrete structure, the design rationality and construction safety of the high and large formwork supporting system are improved, and intelligent and digital whole-process management and control are achieved.

The invention discloses a strength simulation and checking method for a vacuum chamber Sector lifting appliance of a nuclear fusion device, which comprises the following steps of: S1, acquiring structural data of the vacuum chamber Sector lifting appliance, establishing a corresponding geometric data model, and establishing a finite element model to simulate the geometric data model, wherein the geometric data model is set as a lifting appliance three-dimensional model containing all key force bearing components; s2, defining a specified load working condition set, and performing stress analysis and buckling instability analysis operation on the specified component model under the specified load working condition; s3, the safety margin of the specified component model is calculated and evaluated so as to check and judge whether the specified structure meets the specified strength requirement or not, and the safety margin of the specified component model has the following double-threshold combined criterion. According to the invention, systematic mechanical evaluation of key structures such as a lifting lug, a pull rod and a bridge can be realized, so that the structural safety and reliability in a lifting process are guaranteed.

The invention discloses a modularized replaceable self-resetting energy consumption type bridge pier anti-collision system, and belongs to the technical field of bridge engineering structure safety. The system includes an annular rubber pad, an annularly disposed damper module, and a shape memory alloy (SMA) tow. The damper module comprises a cylinder body, high-viscosity fluid, a reset spring, a piston and a force transmission plate. The cylinder body is fixed to the pier in a surrounding mode through bolts and rubber pads, the inner wall of the cylinder body is connected with the piston through a reset spring, the piston rod end is hinged to the force transmission plates, and the SMA wire bundles are connected with the force transmission plates. When an external impact load acts on the force transmission plate, the fluid damper provides viscous energy consumption, and the SMA wire bundle, the spring and the rubber pad provide deformation energy consumption; after the collision is finished, the system can be reset to the initial position under the synergistic effect of the SMA wire bundle, the spring and the rubber pad. By means of the modular design, efficient energy consumption and self-resetting can be achieved; and meanwhile, a single damper can be quickly replaced after being damaged, so that the maintenance efficiency and the system recoverability are greatly improved.

The invention provides a bridge monitoring data abnormal value intelligent judgment and processing system, which comprises a monitoring module used for acquiring dynamic displacement data of a plurality of monitoring points of a bridge in a non-contact manner; the preprocessing module is used for performing time synchronization and quality fusion on the displacement, load and environment data to generate multi-dimensional time sequence data; the anomaly recognition module is used for extracting collaborative deformation and power fingerprint features and performing anomaly recognition and initial classification based on the feature deviation degree; the abnormality diagnosis module is used for performing abnormality cause analysis in combination with the load and the environmental condition and outputting an abnormality diagnosis result; and the strategy generation module is used for generating a grading early warning and disposal strategy according to the diagnosis result and the structural key index usage degree. The accuracy and efficiency of bridge structure safety monitoring can be improved, and safe operation of a bridge is guaranteed.

The invention relates to the technical field of wind power prestress monitoring, in particular to an intelligent monitoring system, method, equipment and medium for the prestress of a wind power reinforced concrete tower drum, and the system comprises a monitoring module which is used for obtaining multi-source structure state data from a plurality of sensors on the wind power reinforced concrete tower drum; the processing unit is connected with the monitoring module; the processing unit is configured to perform data processing on the multi-source structure state data; based on the processed multi-source structure state data, constructing a prestress state evaluation model used for representing an association relationship between the prestress data and the structure health index data; and according to an output result of the prestress state evaluation model, triggering a multi-stage early warning module, and generating an early warning signal corresponding to the output result. By constructing a multi-sensor fusion monitoring network, correlation analysis of prestress data and structural health indexes is realized, and a comprehensive basis is provided for safety assessment and maintenance of the tower drum structure.

The invention discloses a water gate structure safety monitoring system and method, belongs to the technical field of computer communication, breaks through isolated parameter monitoring limitation, constructs multi-physics field dynamic coupling analysis capability, carries out deep fusion processing on multi-source data, improves data reliability and defect identification precision in a complex environment, and improves the safety of the water gate structure. And the use effect of real-time quantitative evaluation of the structural state is realized by utilizing the technical means of dynamic model inversion and parameter calibration, that is, a stress-deformation-leakage three-dimensional finite element coupling model is constructed, dynamic calibration of material parameters and real-time inversion of the structural state are realized in combination with long-term monitoring data, the limitation of isolation of traditional monitoring is broken through, and the real-time quantitative evaluation of the structural state is realized. And a more accurate technical support is provided for water gate safety assessment.